INTEGRATING GEOSPATIAL DATA FOR BUSINESS ACCOMMODATION ANALYSIS: A CASE STUDY OF GREATER ESSEX
THE PROCESSED DATA VOA DATASET
#Install packages
!pip install openpyxl
!pip install pandas
!pip install plotly.express
!pip install folium
!pip install seaborn
!pip install matplotlib
!pip install geopy
!pip install tabulate
!pip install opencage
!pip install geopandas
!pip install rpy2
!pip install mapclassify
!pip install pathlib
!pip install contextily geopandas
!pip install -U kaleido
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in c:\users\my computer\anaconda3\lib\site-packages (0.4.1) Requirement already satisfied: pandas>=0.20.0 in c:\users\my computer\anaconda3\lib\site-packages (from plotly.express) (2.1.4) Requirement already satisfied: plotly>=4.1.0 in c:\users\my computer\anaconda3\lib\site-packages (from plotly.express) (5.9.0) Requirement already satisfied: statsmodels>=0.9.0 in c:\users\my computer\anaconda3\lib\site-packages (from plotly.express) (0.14.0) Requirement already satisfied: scipy>=0.18 in c:\users\my computer\anaconda3\lib\site-packages (from plotly.express) (1.11.4) Requirement already satisfied: patsy>=0.5 in c:\users\my computer\anaconda3\lib\site-packages (from plotly.express) (0.5.3) Requirement already satisfied: numpy>=1.11 in c:\users\my computer\anaconda3\lib\site-packages (from plotly.express) (1.26.4) Requirement already satisfied: python-dateutil>=2.8.2 in c:\users\my computer\anaconda3\lib\site-packages (from pandas>=0.20.0->plotly.express) (2.8.2) Requirement already 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LOAD THE INSTALLED PYTHON PACKAGES
#Load libraries from installed python packages
import pandas as pd
import plotly.express as px
import folium
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from folium.plugins import MarkerCluster
from geopy.geocoders import Nominatim
from geopy.extra.rate_limiter import RateLimiter
from tabulate import tabulate
import time
from datetime import timedelta
from opencage.geocoder import OpenCageGeocode
from io import BytesIO
import base64
import geopandas as gpd
from folium import plugins
from shapely.geometry import Polygon
from shapely.geometry import Point
import numpy as np
import branca
from folium import IFrame
from plotly.offline import plot
import datetime
import mapclassify
from mapclassify import Quantiles, UserDefined
from pathlib import Path
from folium import GeoJson
from shapely import wkt
from sklearn.neighbors import BallTree
import contextily as ctx
from matplotlib.colors import ListedColormap
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
%matplotlib inline
BASE = Path('.').resolve() #Set path
import os
import kaleido
import mapclassify as mc
import warnings
SUMMARY ANALYSIS
Package Installation: A wide range of Python packages are installed using pip, crucial for data manipulation, visualization, geographical data handling, and extensive statistical analysis. This installation process ensures all dependencies are met, as many of these packages rely on other libraries.
Dependencies: The installation process ensures that all dependencies are met, as many of these packages depend on each other. For example, plotly.express depends on pandas and plotly, while geopandas relies on numpy, pandas, and shapely.
Package Loading After installation, the packages are imported into the Python environment, ready to be used for specific tasks:
1.pandas:
Purpose: This package is for data manipulation and analysis, highly efficient in handling large datasets, and performing operations such as data cleaning, transformation, and aggregation.
Dependencies: The pandas package depends on libraries like numpy for numerical operations.
2.plotly.express, plotly.graph_objects, plotly.subplots:
Purpose: These are tools for creating statistical and interactive visualizations. Plotly provides a flexible and easy-to-use framework for generating interactive plots Dependencies: These packages rely on pandas for handling data, and on plotly as the core library.
3.folium:
Purpose: This is a powerful tool for creating interactive maps and visualizing geographical data.
Dependencies: This package depends on various geospatial libraries such as geopandas and shapely.
4.seaborn and matplotlib.pyplot:
Purpose: For data visualization. The Seaborn tool provides a high-level interface for drawing attractive statistical graphics, while matplotlib is a comprehensive library for creating static, animated, and interactive visualizations.
Dependencies: Seaborn depends on matplotlib, pandas, and numpy packages.
5.geopy and opencage:
Purpose: These are for geocoding and geographical data handling tools, used to convert addresses into geographical coordinates and vice versa.
Dependencies: These tools depend on requests and other HTTP libraries for API interactions.
6.geopandas, shapely, contextily:
Purpose: These are tools for working with geospatial data. Geopandas extends pandas with spatial data types, shapely facilitates the manipulation and analysis of planar geometric objects, and contextily is used to add basemaps to geopandas plots.
Dependencies: Geopandas depends on numpy, pandas, shapely, and Fiona. Contextily depends on matplotlib and rasterio.
7.tabulate:
Purpose: This is a tool for displaying data in a readable tabular format, making it easier to view and interpret data within a console or notebook environment.
8.rpy2:
Purpose: This tool allows the integration of R within Python, enabling the use of R’s statistical capabilities directly within Python.
Dependencies: The rpy2 relies on the R programming environment and requires R to be installed on the system.
9.mapclassify:
Purpose: This tool provides classification schemes for choropleth maps, particularly useful for statistical mapping and data binning.
Dependencies: The mapclassify works closely with geopandas and other geospatial libraries.
10.pathlib:
Purpose: This is a module for handling and manipulating filesystem paths in an object-oriented manner.
Dependencies: It's a part of the Python standard library and does not have external dependencies.
11.kaleido:
Purpose: This is lightweight dependency for exporting Plotly figures to static images, useful for saving interactive plots as images.
Dependencies: It integrates seamlessly with Plotly.
12.shapely:
Purpose: Shapely provides geometric objects, such as points, lines, and polygons, and operations on these objects, such as buffering, intersections, and unions.
Dependencies: It depends on libraries like numpy and is a core dependency of geopandas.
13.contextily:
Purpose: This tool is used to add background tiles to geospatial plots made with geopandas. It helps contextualize spatial data by providing map tiles.
Dependencies: contextily with geopandas, matplotlib, and other mapping libraries.
14.mpl_toolkits.axes_grid1:
Purpose: This is a toolkit that extends matplotlib's capabilities, allowing for the creation of more complex layouts like inset axes.
Dependencies: This package requires matplotlib.
15.sklearn.neighbors.BallTree:
Purpose: This tool implements a tree data structure for efficient neighbor searches, often used in spatial analysis to find points within a certain distance of other points.
Dependencies: This is part of the scikit-learn library, which depends on numpy and scipy.
16.matplotlib.colors.ListedColormap:
Purpose: This is a utility in matplotlib for creating custom color maps, useful for controlling the color schemes used in plots.
Dependencies: This is part of the matplotlib library.
17.warnings:
Purpose: This is a built-in Python module that provides a way to issue warning messages and control their behavior.
18.os:
Purpose: This is a standard library module that provides functions for interacting with the operating system, such as file and directory manipulation.
19.time, datetime:
Purpose: These are built-in Python modules for handling time and date operations. time provides time-related functions, while datetime is more comprehensive for date and time manipulation.
20.numpy:
Purpose: This is a fundamental package for scientific computing in Python, providing support for arrays, matrices, and a large collection of mathematical functions.
Dependencies: numpy is core to many libraries like pandas, scipy, and more.
21.branca:
Purpose: This tool is used for generating HTML and JavaScript elements, often used in conjunction with folium for adding custom popups and controls to maps.
Dependencies: branca integrates with folium and other web-based tools.
22.io, base64:
Purpose: io is a core module for handling file-like objects, while base64 provides functions for encoding binary data to ASCII characters.
23.mapclassify (Quantiles, UserDefined):
Purpose: These functions within mapclassify provide methods for statistical classification of data, essential for creating meaningful choropleth maps.
24.sklearn.neighbors.BallTree:
Purpose: This package is a part of scikit-learn, used for nearest neighbor searches, often employed in spatial data analysis.
25.shapely.geometry.Polygon, shapely.geometry.Point:
Purpose: These are modules within Shapely for creating and manipulating geometric objects like polygons and points.
26.inset_axes (mpl_toolkits.axes_grid1.inset_locator):
Purpose: These packages allows the creation of inset axes in a matplotlib figure, useful for adding small maps or charts within a larger plot.
27.plotly.offline.plot:
Purpose: This tool is used for rendering Plotly figures in offline mode, which is particularly useful for saving or displaying plots in environments without internet access.
28.Nominatim (geopy.geocoders), RateLimiter (geopy.extra.rate_limiter):
Purpose: Nominatim is a geocoding tool that converts geographic coordinates into readable addresses and vice versa. RateLimiter helps to control the rate of API requests, preventing exceeding usage limits.
29.%matplotlib inline: This command ensures that plots generated with matplotlib are displayed directly in the Jupyter notebook, enhancing the workflow by allowing for immediate visual feedback.
This setup of Python packages provides a comprehensive environment for performing advanced data analysis, visualization, and geospatial analysis within Python.numpy, pandas, shapely).
DATA PREPROCESSING
IDENTIFY A FILE PATH AND LOAD THVOA E DATASET:
#Identify the file path
Data_path="C:/Users/MY COMPUTER/OneDrive/Desktop/DISSERTATION/Processed Data VOA.xlsx"
#Load the excel file
Processed_Data_VOA=pd.read_excel(Data_path)
PRINT THE ROWS AND COLUMNS OF THE VOA EXCEL FILE TO UNDERSTAND THE STRUCTURE OF THE DATASET:
#Display the first few rows of the data frame to understand its structure
print("\nTHE FIRST FEW ROWS OF THE PROCESSED_DATA_VOA DATASET:")
print(Processed_Data_VOA.head())
#Display the columns of the data frame
print("\nTHE COLUMNS OF THE PROCESSED_DATA_VOA DATASET:")
print(Processed_Data_VOA.columns)
THE FIRST FEW ROWS OF THE PROCESSED_DATA_VOA DATASET:
RecordType AssessmentReference UARN BillingAuthCode \
0 1 24395587000 10007081000 1535
1 1 24395588000 10007084000 1535
2 1 24395586000 10007077000 1535
3 1 24395589000 10007092000 1535
4 1 24395593000 10007105000 1535
FirmName StreetNameNum Address1 Address3 Street \
0 The Occupier SUITE 6 1ST FLR 8/10 NaN NaN HIGH BEECH ROAD
1 The Occupier SUITE 7 1ST FLR 8/10 NaN NaN HIGH BEECH ROAD
2 The Occupier SUITE 5 1ST FLR 8/10 NaN NaN HIGH BEECH ROAD
3 The Occupier SUITE 8 1ST FLR 8/10 NaN NaN HIGH BEECH ROAD
4 The Occupier SUITE 12 2ND FLR 8/10 NaN NaN HIGH BEECH ROAD
Town ... 2023 2024 2025 AreaCombined_Group RateValue \
0 NaN ... 1 1 1 1. <1,500 sq ft (<c.150 sq m) 3700
1 NaN ... 1 1 1 1. <1,500 sq ft (<c.150 sq m) 5800
2 NaN ... 1 1 1 1. <1,500 sq ft (<c.150 sq m) 6100
3 NaN ... 1 1 1 1. <1,500 sq ft (<c.150 sq m) 3950
4 NaN ... 1 1 1 1. <1,500 sq ft (<c.150 sq m) 2600
RV Quartile Max Value Quartiles Original_FromDate Original_ToDate \
0 4 155.61 2023-04-01 NaT
1 4 155.61 2023-04-01 NaT
2 4 155.61 2023-04-01 NaT
3 4 155.61 2023-04-01 NaT
4 4 155.61 2023-04-01 NaT
FromDate2
0 2023-04-01
1 2023-04-01
2 2023-04-01
3 2023-04-01
4 2023-04-01
[5 rows x 57 columns]
THE COLUMNS OF THE PROCESSED_DATA_VOA DATASET:
Index([ 'RecordType', 'AssessmentReference', 'UARN',
'BillingAuthCode', 'FirmName', 'StreetNameNum',
'Address1', 'Address3', 'Street',
'Town', 'PostDistrict', 'Local_Authority',
'County', 'Postcode', 'SchemeRef',
'PriDescText', 'Area', 'AreaSqM',
'AreaSqF', '£/SqF', '£/Sqm',
'SubTotal', 'TotalVal', 'AdoptedRV',
'ListYear', 'BAName', 'BARef',
'VORef', 'FromDate', 'ToDate',
'SCATCode', 'ECC Cat', 'Unit',
'UnAdjPrice', 2010, 2011,
2012, 2013, 2014,
2015, 2016, 2017,
2018, 2019, 2020,
2021, 2022, 2023,
2024, 2025, 'AreaCombined_Group',
'RateValue', 'RV Quartile', 'Max Value Quartiles',
'Original_FromDate', 'Original_ToDate', 'FromDate2'],
dtype='object')
DISPLAY THE SUMMARY STATISTICS OF THE PROCESSED_DATA_VOA DATASET:
#Print summary statistics including numerical and categorical columns
print("SUMMARY STATISTICS OF THE PROCESSED_DATA_VOA DATASET:")
print(Processed_Data_VOA.describe(include='all'))
SUMMARY STATISTICS OF THE PROCESSED_DATA_VOA DATASET:
RecordType AssessmentReference UARN BillingAuthCode \
count 96158.0 9.615800e+04 9.615800e+04 96158.000000
unique NaN NaN NaN NaN
top NaN NaN NaN NaN
freq NaN NaN NaN NaN
mean 1.0 1.896135e+10 6.499285e+09 1541.079317
min 1.0 9.236290e+09 1.330230e+05 1505.000000
25% 1.0 1.468264e+10 1.525043e+09 1520.000000
50% 1.0 1.829209e+10 7.041802e+09 1535.000000
75% 1.0 2.467076e+10 1.033491e+10 1560.000000
max 1.0 2.749686e+10 1.451564e+10 1595.000000
std 0.0 5.533181e+09 4.463310e+09 28.074587
FirmName StreetNameNum Address1 Address3 Street \
count 82819 95994.0 0.0 28131 95181
unique 533 28973.0 NaN 1237 2743
top The Occupier 1.0 NaN THE MALTINGS HIGH STREET
freq 81500 916.0 NaN 352 2706
mean NaN NaN NaN NaN NaN
min NaN NaN NaN NaN NaN
25% NaN NaN NaN NaN NaN
50% NaN NaN NaN NaN NaN
75% NaN NaN NaN NaN NaN
max NaN NaN NaN NaN NaN
std NaN NaN NaN NaN NaN
Town ... 2023 2024 2025 \
count 35798 ... 96158.000000 96158.000000 96158.000000
unique 370 ... NaN NaN NaN
top STANSTED AIRPORT ... NaN NaN NaN
freq 823 ... NaN NaN NaN
mean NaN ... 0.297157 0.296918 0.296918
min NaN ... 0.000000 0.000000 0.000000
25% NaN ... 0.000000 0.000000 0.000000
50% NaN ... 0.000000 0.000000 0.000000
75% NaN ... 1.000000 1.000000 1.000000
max NaN ... 1.000000 1.000000 1.000000
std NaN ... 0.457009 0.456902 0.456902
AreaCombined_Group RateValue RV Quartile \
count 96158 9.615800e+04 96158.000000
unique 4 NaN NaN
top 1. <1,500 sq ft (<c.150 sq m) NaN NaN
freq 53294 NaN NaN
mean NaN 2.524186e+04 2.513426
min NaN 0.000000e+00 1.000000
25% NaN 3.700000e+03 2.000000
50% NaN 7.900000e+03 3.000000
75% NaN 1.775000e+04 4.000000
max NaN 1.239000e+07 4.000000
std NaN 1.202073e+05 1.118499
Max Value Quartiles Original_FromDate \
count 96158.000000 96158
unique NaN NaN
top NaN NaN
freq NaN NaN
mean 78.667753 2017-08-18 13:32:13.991971584
min 1.190000 2009-09-14 00:00:00
25% 4.940000 2013-07-30 00:00:00
50% 8.170000 2017-04-01 00:00:00
75% 32.520000 2023-04-01 00:00:00
max 1416.770000 2023-11-28 00:00:00
std 263.043464 NaN
Original_ToDate FromDate2
count 12768 96158
unique NaN NaN
top NaN NaN
freq NaN NaN
mean 2017-02-16 06:47:01.804511488 2017-08-05 02:42:19.071112192
min 2010-04-07 00:00:00 2009-09-14 00:00:00
25% 2014-03-13 00:00:00 2013-07-30 00:00:00
50% 2016-12-28 00:00:00 2017-04-01 00:00:00
75% 2020-02-23 00:00:00 2023-04-01 00:00:00
max 2023-11-26 00:00:00 2023-11-23 00:00:00
std NaN NaN
[11 rows x 57 columns]
DISPLAY THE COLUMN ENTRIES AND THEIR DATA TYPES, NUMBER OF ROWS/COLUMNS, AND CONFIRM IF THERE ARE DUPLICATE ROWS IN THE DATASET:
#Print the number of non-null entries and the data types of each column
print("\nPROCESSED_DATA_VOA INFORMATION:")
print(Processed_Data_VOA.info())
#Identify the number of rows and columns in the dataset
num_rows, num_cols=Processed_Data_VOA.shape
print(f"THE NUMBER OF ROWS IN PROCESSED_DATA_VOA DATASET: {num_rows}.", f"THE NUMBER OF COLUMNS IN PROCESSED_DATA_VOA DATASET(indexes): {num_cols}")
#Check for duplicate rows in the dataset
duplicates = Processed_Data_VOA.duplicated()
print(f"THE NUMBER OF DUPLICATE ROWS IN PROCESSED_DATA_VOA DATASET: {duplicates.sum()}")
PROCESSED_DATA_VOA INFORMATION: <class 'pandas.core.frame.DataFrame'> RangeIndex: 96158 entries, 0 to 96157 Data columns (total 57 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 RecordType 96158 non-null int64 1 AssessmentReference 96158 non-null int64 2 UARN 96158 non-null int64 3 BillingAuthCode 96158 non-null int64 4 FirmName 82819 non-null object 5 StreetNameNum 95994 non-null object 6 Address1 0 non-null float64 7 Address3 28131 non-null object 8 Street 95181 non-null object 9 Town 35798 non-null object 10 PostDistrict 96158 non-null object 11 Local_Authority 96158 non-null object 12 County 68004 non-null object 13 Postcode 96158 non-null object 14 SchemeRef 96158 non-null int64 15 PriDescText 96158 non-null object 16 Area 96158 non-null float64 17 AreaSqM 96158 non-null int64 18 AreaSqF 96158 non-null int64 19 £/SqF 96158 non-null float64 20 £/Sqm 96158 non-null float64 21 SubTotal 96158 non-null int64 22 TotalVal 96158 non-null int64 23 AdoptedRV 96158 non-null int64 24 ListYear 96158 non-null int64 25 BAName 96158 non-null object 26 BARef 96158 non-null object 27 VORef 96158 non-null int64 28 FromDate 96158 non-null datetime64[ns] 29 ToDate 96158 non-null datetime64[ns] 30 SCATCode 96158 non-null int64 31 ECC Cat 96158 non-null object 32 Unit 96158 non-null object 33 UnAdjPrice 96158 non-null float64 34 2010 96158 non-null int64 35 2011 96158 non-null int64 36 2012 96158 non-null int64 37 2013 96158 non-null int64 38 2014 96158 non-null int64 39 2015 96158 non-null int64 40 2016 96158 non-null int64 41 2017 96158 non-null int64 42 2018 96158 non-null int64 43 2019 96158 non-null int64 44 2020 96158 non-null int64 45 2021 96158 non-null int64 46 2022 96158 non-null int64 47 2023 96158 non-null int64 48 2024 96158 non-null int64 49 2025 96158 non-null int64 50 AreaCombined_Group 96158 non-null object 51 RateValue 96158 non-null int64 52 RV Quartile 96158 non-null int64 53 Max Value Quartiles 96158 non-null float64 54 Original_FromDate 96158 non-null datetime64[ns] 55 Original_ToDate 12768 non-null datetime64[ns] 56 FromDate2 96158 non-null datetime64[ns] dtypes: datetime64[ns](5), float64(6), int64(31), object(15) memory usage: 41.8+ MB None THE NUMBER OF ROWS IN PROCESSED_DATA_VOA DATASET: 96158. THE NUMBER OF COLUMNS IN PROCESSED_DATA_VOA DATASET(indexes): 57 THE NUMBER OF DUPLICATE ROWS IN PROCESSED_DATA_VOA DATASET: 0
DISPLAY THE DESCRIPTIVE STATISTICS OF NUMERICAL AND CATEGORICAL COLUMNS IN THE VOA DATASET:
#Descriptive statistics for numerical columns
print("\nDESCRIPTIVE STATISTICS FOR NUMERICAL COLUMNS IN PROCESSED_DATA_VOA DATASET:")
print(Processed_Data_VOA.describe())
#Descriptive statistics for categorical columns
print("\nDESCRIPTIVE STATISTICS FOR CATEGORICAL COLUMNS IN PROCESSED_DATA_VOA DATASET:")
print(Processed_Data_VOA.describe(include=['category', 'object']))
DESCRIPTIVE STATISTICS FOR NUMERICAL COLUMNS IN PROCESSED_DATA_VOA DATASET:
RecordType AssessmentReference UARN BillingAuthCode \
count 96158.0 9.615800e+04 9.615800e+04 96158.000000
mean 1.0 1.896135e+10 6.499285e+09 1541.079317
min 1.0 9.236290e+09 1.330230e+05 1505.000000
25% 1.0 1.468264e+10 1.525043e+09 1520.000000
50% 1.0 1.829209e+10 7.041802e+09 1535.000000
75% 1.0 2.467076e+10 1.033491e+10 1560.000000
max 1.0 2.749686e+10 1.451564e+10 1595.000000
std 0.0 5.533181e+09 4.463310e+09 28.074587
Address1 SchemeRef Area AreaSqM AreaSqF \
count 0.0 96158.000000 96158.000000 96158.000000 9.615800e+04
mean NaN 355663.736257 477.688791 477.241405 5.141289e+03
min NaN 78890.000000 0.500000 0.000000 5.000000e+00
25% NaN 124933.000000 47.600000 47.000000 5.120000e+02
50% NaN 369426.000000 115.900000 115.000000 1.247000e+03
75% NaN 589579.000000 298.285000 298.000000 3.210000e+03
max NaN 646957.000000 201898.790000 201898.000000 2.173218e+06
std NaN 198953.022647 2358.820384 2358.822732 2.539011e+04
£/SqF ... 2022 2023 2024 \
count 96158.000000 ... 96158.000000 96158.000000 96158.000000
mean 8.355894 ... 0.308097 0.297157 0.296918
min 0.000000 ... 0.000000 0.000000 0.000000
25% 4.310345 ... 0.000000 0.000000 0.000000
50% 6.608805 ... 0.000000 0.000000 0.000000
75% 10.170978 ... 1.000000 1.000000 1.000000
max 1525.000000 ... 1.000000 1.000000 1.000000
std 11.465130 ... 0.461709 0.457009 0.456902
2025 RateValue RV Quartile Max Value Quartiles \
count 96158.000000 9.615800e+04 96158.000000 96158.000000
mean 0.296918 2.524186e+04 2.513426 78.667753
min 0.000000 0.000000e+00 1.000000 1.190000
25% 0.000000 3.700000e+03 2.000000 4.940000
50% 0.000000 7.900000e+03 3.000000 8.170000
75% 1.000000 1.775000e+04 4.000000 32.520000
max 1.000000 1.239000e+07 4.000000 1416.770000
std 0.456902 1.202073e+05 1.118499 263.043464
Original_FromDate Original_ToDate \
count 96158 12768
mean 2017-08-18 13:32:13.991971584 2017-02-16 06:47:01.804511488
min 2009-09-14 00:00:00 2010-04-07 00:00:00
25% 2013-07-30 00:00:00 2014-03-13 00:00:00
50% 2017-04-01 00:00:00 2016-12-28 00:00:00
75% 2023-04-01 00:00:00 2020-02-23 00:00:00
max 2023-11-28 00:00:00 2023-11-26 00:00:00
std NaN NaN
FromDate2
count 96158
mean 2017-08-05 02:42:19.071112192
min 2009-09-14 00:00:00
25% 2013-07-30 00:00:00
50% 2017-04-01 00:00:00
75% 2023-04-01 00:00:00
max 2023-11-23 00:00:00
std NaN
[8 rows x 42 columns]
DESCRIPTIVE STATISTICS FOR CATEGORICAL COLUMNS IN PROCESSED_DATA_VOA DATASET:
FirmName StreetNameNum Address3 Street \
count 82819 95994 28131 95181
unique 533 28973 1237 2743
top The Occupier 1 THE MALTINGS HIGH STREET
freq 81500 916 352 2706
Town PostDistrict Local_Authority County Postcode \
count 35798 96158 96158 68004 96158
unique 370 61 14 3 6632
top STANSTED AIRPORT COLCHESTER Basildon ESSEX CO7 0AR
freq 823 13130 9593 65418 524
PriDescText BAName BARef ECC Cat \
count 96158 96158 96158 96158
unique 609 14 42191 3
top Offices And Premises Basildon 700030139001455 Industrial - General
freq 34080 9593 3 45495
Unit AreaCombined_Group
count 96158 96158
unique 6 4
top GIA 1. <1,500 sq ft (<c.150 sq m)
freq 56819 53294
SUMMARY ANALYSIS FOR THE DATA PREPROCESSING OF THE PROCESSED_DATA_VOA DATASET
The Processed_Data_VOA is a comprehensive dataset containing numerical and categorical data structured with 57 columns, including various fields relevant to property and assessment data. The summary statistics provide a good overview of the data's characteristics, highlighting key aspects such as the range and distribution of values in numerical columns and the frequency of values in categorical columns. There are no duplicate rows in the dataset, and all columns have non-null entries, ensuring data completeness.
DATA LOADIN T the dataset is loaded from an Excel file located a"C:/Users/MY COMPUTER/OneDrive/Desktop/DISSERTATION/Processed Data VOA.xlsx"sx into a pandas DataFrame nam d Processed_Data_VOA.
STRUCTURE OF THE D TA: the dataset has 57 columns including both numerical and categorical da ta. Some of the firewfive rows are RecordType, AssessmentReference, UARN, BillingAuthCode, FirmName, StreetNameNum, Address1, Address3, Street, Town, etc.
Numerical columns inRecordType, AssessmentReference, UARN, BillingAuthCode, Address1, SchemeRef, Area, AreaSqM, AreaSqF, E/SqF, 2022, 2023, 2024, 2025, RateValue, RV Quantile, Max Value Quartiles, Original_FromDate, Original_ToDate, FromDate2., etc.
Categorical columns include FiStreetNameNum, Address3, Street, Town, PostDistrict, Local_Authority, county, Postcode, PriDescText, BAName, BARef, ECC Cat, Unit, AreaCombined_Group.wn, etc.
SUMMARY STATISTICS FOR NUMERICAL COLU1.MNS ARE:
RecordType: All entries have a value of 1, indicating uniformity in t2.his field.
AssessmentReference: The mean is approximately 1.9e10 with a standard deviation of about 5.5e9. The values range from 9.2e3.9 to 2.7e10.
UARN: The mean is approximately 6.5e9 with a standard deviation of about 4.5e9. The values range from 1.4.3e5 to 1.4e10.
BillingAuthCode: The mean is 1541 with a standard deviation of 28. Values range fr5.om 1505 to 1595.
StreetNameNum: The mean is 18.64 with a standard deviation of about 53.74. Values ran6.ge from 1 to 1759.
Address1: All entries7. have a value of -1.
SchemeRef: The mean is 355663 with a standard deviation of approximately 198953. Values range8. from 78890 to 646957.
Area: The mean area is approximately 477.69 with a standard deviation of about 2358.82. Values rang9.e from 0.5 to 201898.79.
AreaSqM: The mean is 477.24 and a standard deviation of about 2358.82. Valu10.es range from 0 to 201898.
AreaSqF: The mean is 5141.29 with a standard deviation of approximately 25390.11. Val11.ues range from 5 to 2173218.
RateValue: The mean is 25241.86 with a standard deviation of about 120207.3. Va12.lues range from 0 to 12390000.
RV Quartile: The mean is 2.51 with a standard deviation of approximately13. 1.12. Values range from 1 to 4.
Max Value Quartiles: The mean is 78.67 with a standard deviation of approximately 263.04. 14.15.Values range from 1.19 to 1416.77.
Years (2010-2025): Most years have binary values indicating presence (1) or absence (0) of data. The mean values are 0.30 wit16.h standard deviations close to 0.46.
Original_FromDate: The mean date is approximately 2017-08-18. Date17.s range from 2009-09-14 to 2023-11-28.
Original_ToDate: The mean date is approximately 2018-09-06. Da18.tes range from 2010-04-07 to 2024-11-22.
FromDate2: The mean date is approximately 2017-08-05 . Dates range from 2009-09-14 to 2023-11-23.
SUM1.MARY STATISTICS FOR CATEGORICAL COLUMNS ARE:
FirmName: There are 533 unique firm names, with "The Occupier" 2.being the most frequent, appearing 94839 times
Address3: There are 1238 unique entries, with "Unknown"3. being the most frequent, appearing 68027 times.
Street: There are 2743 unique street names, with "HIGH STRE4.ET" being the most frequent, appearing 3683 times.
Town: There are 370 unique towns, with "STANSTED AIRP5.ORT" being the most frequent, appearing 61183 times.
PostDistrict: There are 61 unique post districts, with "COLCH6.ESTER" being the most frequent, appearing 13130 times.
Local_Authority: There are 14 unique local authorities, with "7.Basildon" being the most frequent, appearing 9593 times.
County: There are 3 unique counties, wi8.th "ESSEX" being the most frequent, appearing 93572 times.
Postcode: There are 6632 unique postcodes, 9.with "CO7 0AR" being the most frequent, appearing 524 times.
PriDescText: There are 609 unique descriptions, with "Offices10. And Premises" being the most frequent, appearing 34080 times.
BAName: There are 14 unique BA names11., with "Basildon" being the most frequent, appearing 9593 times.
BARef: There are 42191 unique BA references, 12.with "700030139001455" being the most frequent, appearing 3 times.
ECC Cat: There are 3 unique ECC categories, with "I13.ndustrial - General" being the most frequent, appearing 45495 times.
Unit: There are 6 unique 14.unit types, with "GIA" being the most frequent, appearing 56819 times.
AreaCombined_Group: There are 4 unique area combined groups, with "1. < 1,500 sq ft (<c.150 sq m)" being the mosProcessed_Data_VOA t frequent, appearing 53294 times.
DATA TYPES AND NON-NULL ENTRIES:
The data RecordType, AssessmentReference, UARN, BillingAUTHCode, SchemeRef, AreaSqM, AreaSqF, RateValue, RVQuartile, SubTotal, TotalVal, AdoptedRV, ListYear, VORef, SCATCode, Years(2010-2025).mentReference, UARE/SqF, Max Value Quartiles, E/SqM, UnAdjPrice, Area.ress1, £/SqF, £/Sqm, SubTotalFirmName, StreetNameNum, Address3, Street, Town, PostDistrict, Local_Authority, County, Postcode, PriDescText, BAName, BARef, ECC Cat, Unit, AreaCombined_Group.rity, County, Postcode, PriDescText, BAName, BARef, ECC Cat, Unit, AreaCombined_Group.
Datetime: such as FromDate, ToDate, Original_FromDate, Original_ToDate, FromDate2.
Duplicate Rows: the dataset has no duplicate rows.
CREATING A DATA DICTIONARY TO DISPLAY DESCRIPTIVE INFORMATION OF THE COLUMNS IN THE PROCESSED_DATA_VOA DATASET:
#Print all column names as list for clear visualization
Column_names=Processed_Data_VOA.columns.to_list()
for col in Column_names:
print(col)
RecordType AssessmentReference UARN BillingAuthCode FirmName StreetNameNum Address1 Address3 Street Town PostDistrict Local_Authority County Postcode SchemeRef PriDescText Area AreaSqM AreaSqF £/SqF £/Sqm SubTotal TotalVal AdoptedRV ListYear BAName BARef VORef FromDate ToDate SCATCode ECC Cat Unit UnAdjPrice 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 AreaCombined_Group RateValue RV Quartile Max Value Quartiles Original_FromDate Original_ToDate FromDate2
#Create a dictionary containing the description of each column in the dataset
Column_dictionary = {
"RecordType": "Indicates the type of record, such as assessment or billing.",
"AssessmentReference": "unique reference number assigned to each assessment.",
"UARN": "Unique Assessment Reference Number for each property.",
"BillingAuthCode": "Code representing the billing authority.",
"FirmName": "Name of the firm or occupier of the property.",
"StreetNameNum": "Street name and number of the property.",
"Address1": "Additional address information.",
"Address3": "Additional address information.",
"Street": "Street name where the property is located.",
"Town": "Town where the property is located.",
"PostDistrict": "Postal district of the property.",
"Local_Authority": "Name of the local authority responsible for the property.",
"County": "County where the property is located.",
"Postcode": "Postal code of the property.",
"SchemeRef": "Scheme reference number.",
"PriDescText": "Primary description text of the property.",
"Area": "Area of the property.",
"AreaSqM": "Area of the property in square meters.",
"AreaSqF": "Area of the property in square feet.",
"£/SqF": "Value per square foot of the property.",
"£/Sqm": "Value per square meter of the property.",
"SubTotal": "Subtotal value of the property.",
"TotalVal": "Total value of the property.",
"AdoptedRV": "Adopted rateable value of the property.",
"ListYear": "Year of the list in which the property is included.",
"BAName": "Name of the billing authority.",
"BARef": "Reference number for the billing authority.",
"VORef": "Valuation office reference number.",
"FromDate": "Start date of the assessment.",
"ToDate": "End date of the assessment.",
"SCATCode": "Standard classification of assessment code types of the property.",
"ECC Cat": "Essex county council category of the property.",
"Unit": "Unit of measurement used for the property.",
"UnAdjPrice": "Unadjusted price of the property.",
"2010": "Indicator for the year 2010.",
"2011": "Indicator for the year 2011.",
"2012": "Indicator for the year 2012.",
"2013": "Indicator for the year 2013.",
"2014": "Indicator for the year 2014.",
"2015": "Indicator for the year 2015.",
"2016": "Indicator for the year 2016.",
"2017": "Indicator for the year 2017.",
"2018": "Indicator for the year 2018.",
"2019": "Indicator for the year 2019.",
"2020": "Indicator for the year 2020.",
"2021": "Indicator for the year 2021.",
"2022": "Indicator for the year 2022.",
"2023": "Indicator for the year 2023.",
"2024": "Indicator for the year 2024.",
"2025": "Indicator for the year 2025.",
"AreaCombined_Group": "Grouping based on the combined area of the property.",
"RateValue": "Rateable value of the property.",
"RV Quartile": "Quartile of the rateable value.",
"Max Value Quartiles": "Maximum value quartiles.",
"Original_FromDate": "Original start date of the assessment.",
"Original_ToDate": "Original end date of the assessment.",
"FromDate2": "Secondary start date of the assessment."
}
#Convert dictionary into a dataframe for better visualization
Column_dictionary_df=pd.DataFrame(list(Column_dictionary.items()), columns=['Column', 'Description'])
#Display column dictionary dataframe in tabular format
print("DATA DICTIONARY FOR THE DESCRIPTION OF COLUMNS IN THE PROCESSED_DATA_VOA DATASET:")
print(tabulate(Column_dictionary_df,headers='keys',tablefmt='psql'))
#Save the DataFrame to a CSV file in the specified directory
Column_dictionary_df.to_csv('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\Data_dictionary_for_Processed_Data_VOA.csv', index=False)
print("Data has been saved to 'C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\Data_dictionary_for_Processed_Data_VOA.csv'.")
DATA DICTIONARY FOR THE DESCRIPTION OF COLUMNS IN THE PROCESSED_DATA_VOA DATASET: +----+---------------------+-------------------------------------------------------------------+ | | Column | Description | |----+---------------------+-------------------------------------------------------------------| | 0 | RecordType | Indicates the type of record, such as assessment or billing. | | 1 | AssessmentReference | unique reference number assigned to each assessment. | | 2 | UARN | Unique Assessment Reference Number for each property. | | 3 | BillingAuthCode | Code representing the billing authority. | | 4 | FirmName | Name of the firm or occupier of the property. | | 5 | StreetNameNum | Street name and number of the property. | | 6 | Address1 | Additional address information. | | 7 | Address3 | Additional address information. | | 8 | Street | Street name where the property is located. | | 9 | Town | Town where the property is located. | | 10 | PostDistrict | Postal district of the property. | | 11 | Local_Authority | Name of the local authority responsible for the property. | | 12 | County | County where the property is located. | | 13 | Postcode | Postal code of the property. | | 14 | SchemeRef | Scheme reference number. | | 15 | PriDescText | Primary description text of the property. | | 16 | Area | Area of the property. | | 17 | AreaSqM | Area of the property in square meters. | | 18 | AreaSqF | Area of the property in square feet. | | 19 | £/SqF | Value per square foot of the property. | | 20 | £/Sqm | Value per square meter of the property. | | 21 | SubTotal | Subtotal value of the property. | | 22 | TotalVal | Total value of the property. | | 23 | AdoptedRV | Adopted rateable value of the property. | | 24 | ListYear | Year of the list in which the property is included. | | 25 | BAName | Name of the billing authority. | | 26 | BARef | Reference number for the billing authority. | | 27 | VORef | Valuation office reference number. | | 28 | FromDate | Start date of the assessment. | | 29 | ToDate | End date of the assessment. | | 30 | SCATCode | Standard classification of assessment code types of the property. | | 31 | ECC Cat | Essex county council category of the property. | | 32 | Unit | Unit of measurement used for the property. | | 33 | UnAdjPrice | Unadjusted price of the property. | | 34 | 2010 | Indicator for the year 2010. | | 35 | 2011 | Indicator for the year 2011. | | 36 | 2012 | Indicator for the year 2012. | | 37 | 2013 | Indicator for the year 2013. | | 38 | 2014 | Indicator for the year 2014. | | 39 | 2015 | Indicator for the year 2015. | | 40 | 2016 | Indicator for the year 2016. | | 41 | 2017 | Indicator for the year 2017. | | 42 | 2018 | Indicator for the year 2018. | | 43 | 2019 | Indicator for the year 2019. | | 44 | 2020 | Indicator for the year 2020. | | 45 | 2021 | Indicator for the year 2021. | | 46 | 2022 | Indicator for the year 2022. | | 47 | 2023 | Indicator for the year 2023. | | 48 | 2024 | Indicator for the year 2024. | | 49 | 2025 | Indicator for the year 2025. | | 50 | AreaCombined_Group | Grouping based on the combined area of the property. | | 51 | RateValue | Rateable value of the property. | | 52 | RV Quartile | Quartile of the rateable value. | | 53 | Max Value Quartiles | Maximum value quartiles. | | 54 | Original_FromDate | Original start date of the assessment. | | 55 | Original_ToDate | Original end date of the assessment. | | 56 | FromDate2 | Secondary start date of the assessment. | +----+---------------------+-------------------------------------------------------------------+ Data has been saved to 'C:\Users\MY COMPUTER\OneDrive\Desktop\DISSERTATION\Data_dictionary_for_Processed_Data_VOA.csv'.
IDENTIFY PERCENTAGE OF MISSING VALUES IN COLUMNS OF THE VOA DATASET:
#Check the percentage of missing values in individual columns
Missing_vals=Processed_Data_VOA.isnull().mean() *100
print("THE PERCENTAGE OF MISSING VALUES IN INDIVIDUAL COLUMNS OF THE PROCESSED_DATA_VOA:")
print(Missing_vals)
THE PERCENTAGE OF MISSING VALUES IN INDIVIDUAL COLUMNS OF THE PROCESSED_DATA_VOA: RecordType 0.000000 AssessmentReference 0.000000 UARN 0.000000 BillingAuthCode 0.000000 FirmName 13.871961 StreetNameNum 0.170553 Address1 100.000000 Address3 70.745024 Street 1.016036 Town 62.771688 PostDistrict 0.000000 Local_Authority 0.000000 County 29.278895 Postcode 0.000000 SchemeRef 0.000000 PriDescText 0.000000 Area 0.000000 AreaSqM 0.000000 AreaSqF 0.000000 £/SqF 0.000000 £/Sqm 0.000000 SubTotal 0.000000 TotalVal 0.000000 AdoptedRV 0.000000 ListYear 0.000000 BAName 0.000000 BARef 0.000000 VORef 0.000000 FromDate 0.000000 ToDate 0.000000 SCATCode 0.000000 ECC Cat 0.000000 Unit 0.000000 UnAdjPrice 0.000000 2010 0.000000 2011 0.000000 2012 0.000000 2013 0.000000 2014 0.000000 2015 0.000000 2016 0.000000 2017 0.000000 2018 0.000000 2019 0.000000 2020 0.000000 2021 0.000000 2022 0.000000 2023 0.000000 2024 0.000000 2025 0.000000 AreaCombined_Group 0.000000 RateValue 0.000000 RV Quartile 0.000000 Max Value Quartiles 0.000000 Original_FromDate 0.000000 Original_ToDate 86.721854 FromDate2 0.000000 dtype: float64
DISPLAY COLUMNS WITHOUT MISSING VALUES AND COUNTS OF COLUMNS WITH MISSING VALUES IN THE DATASET:
#Print columns without missing values
Columns_without_missingvals=Missing_vals[Missing_vals==0].index.to_list()
print("COLUMNS WITHOUT MISSING VALUES IN PROCESSED_DATA_VOA:")
print(tabulate([[col] for col in Columns_without_missingvals],headers=['Column Name'],tablefmt='psql'))
#Print columns with missing values and their counts
Columns_with_missingvals=Missing_vals[Missing_vals>0]
print("COLUMNS AND COUNTS OF MISSING VALUES IN PROCESSED_DATA_VOA:")
print(tabulate(Columns_with_missingvals.reset_index().values, headers=['Column Name', 'Percentage of Missing Values'], tablefmt='psql'))
COLUMNS WITHOUT MISSING VALUES IN PROCESSED_DATA_VOA: +---------------------+ | Column Name | |---------------------| | RecordType | | AssessmentReference | | UARN | | BillingAuthCode | | PostDistrict | | Local_Authority | | Postcode | | SchemeRef | | PriDescText | | Area | | AreaSqM | | AreaSqF | | £/SqF | | £/Sqm | | SubTotal | | TotalVal | | AdoptedRV | | ListYear | | BAName | | BARef | | VORef | | FromDate | | ToDate | | SCATCode | | ECC Cat | | Unit | | UnAdjPrice | | 2010 | | 2011 | | 2012 | | 2013 | | 2014 | | 2015 | | 2016 | | 2017 | | 2018 | | 2019 | | 2020 | | 2021 | | 2022 | | 2023 | | 2024 | | 2025 | | AreaCombined_Group | | RateValue | | RV Quartile | | Max Value Quartiles | | Original_FromDate | | FromDate2 | +---------------------+ COLUMNS AND COUNTS OF MISSING VALUES IN PROCESSED_DATA_VOA: +-----------------+--------------------------------+ | Column Name | Percentage of Missing Values | |-----------------+--------------------------------| | FirmName | 13.872 | | StreetNameNum | 0.170553 | | Address1 | 100 | | Address3 | 70.745 | | Street | 1.01604 | | Town | 62.7717 | | County | 29.2789 | | Original_ToDate | 86.7219 | +-----------------+--------------------------------+
IMPUTATION METHODS FOR COLUMNS WITH MISSING VALUES IN THE DATASET:
#Check data types of columns for clear visualization for imputation
data_types = Processed_Data_VOA.dtypes
print("DATA TYPES OF COLUMNS IN PPROCESSED_DATA_VOA:")
print(data_types)
#CALL FUNCTIONS FOR IMPUTATION
#Impute FirmName with mode
Processed_Data_VOA['FirmName'] = Processed_Data_VOA['FirmName'].fillna(Processed_Data_VOA['FirmName'].mode()[0])
#Convert StreetNameNum to numeric type if possible, then impute with median
Processed_Data_VOA['StreetNameNum'] = pd.to_numeric(Processed_Data_VOA['StreetNameNum'], errors='coerce') #coerce converts non-numeric values to NaN
Processed_Data_VOA['StreetNameNum'] = Processed_Data_VOA['StreetNameNum'].fillna(Processed_Data_VOA['StreetNameNum'].median())
#Impute Address3 with "Unknown"
Processed_Data_VOA['Address3'] = Processed_Data_VOA['Address3'].fillna('Unknown')
#Impute Street, Town, County with mode
Processed_Data_VOA['Street'] = Processed_Data_VOA['Street'].fillna(Processed_Data_VOA['Street'].mode()[0])
Processed_Data_VOA['Town'] = Processed_Data_VOA['Town'].fillna(Processed_Data_VOA['Town'].mode()[0])
Processed_Data_VOA['County'] = Processed_Data_VOA['County'].fillna(Processed_Data_VOA['County'].mode()[0])
#impute 'Original_ToDate' with 'FromDate' plus 1 year duration
Processed_Data_VOA.loc[:, 'Original_ToDate'] = Processed_Data_VOA['Original_ToDate'].fillna(Processed_Data_VOA['FromDate'] + timedelta(days=365))
DATA TYPES OF COLUMNS IN PPROCESSED_DATA_VOA: RecordType int64 AssessmentReference int64 UARN int64 BillingAuthCode int64 FirmName object StreetNameNum object Address1 float64 Address3 object Street object Town object PostDistrict object Local_Authority object County object Postcode object SchemeRef int64 PriDescText object Area float64 AreaSqM int64 AreaSqF int64 £/SqF float64 £/Sqm float64 SubTotal int64 TotalVal int64 AdoptedRV int64 ListYear int64 BAName object BARef object VORef int64 FromDate datetime64[ns] ToDate datetime64[ns] SCATCode int64 ECC Cat object Unit object UnAdjPrice float64 2010 int64 2011 int64 2012 int64 2013 int64 2014 int64 2015 int64 2016 int64 2017 int64 2018 int64 2019 int64 2020 int64 2021 int64 2022 int64 2023 int64 2024 int64 2025 int64 AreaCombined_Group object RateValue int64 RV Quartile int64 Max Value Quartiles float64 Original_FromDate datetime64[ns] Original_ToDate datetime64[ns] FromDate2 datetime64[ns] dtype: object
CONFIRM IF IMPUTATION METHODS FOR COLUMNS WITH MISSING VALUES IS SUCCESSFUL:
#Check if there are still missing values after imputations
missingvals_firmname_after_imputation = Processed_Data_VOA['FirmName'].isnull().sum()
print(f"NUMBER OF MISSING VALUES IN 'FirmName' AFTER IMPUTATION: {missingvals_firmname_after_imputation}")
missingvals_streetnum_after_imputation = Processed_Data_VOA['StreetNameNum'].isnull().sum()
print(f"NUMBER OF MISSING VALUES IN 'StreetNameNum' AFTER IMPUTATION: {missingvals_streetnum_after_imputation}")
missingvals_address3_after_imputation = Processed_Data_VOA['Address3'].isnull().sum()
print(f"NUMBER OF MISSING VALUES IN 'Address3' AFTER IMPUTATION: {missingvals_address3_after_imputation}")
missingvals_street_after_imputation = Processed_Data_VOA['Street'].isnull().sum()
print(f"NUMBER OF MISSING VALUES IN 'Street' AFTER IMPUTATION: {missingvals_street_after_imputation}")
missingvals_town_after_imputation = Processed_Data_VOA['Town'].isnull().sum()
print(f"NUMBER OF MISSING VALUES IN 'Town' AFTER IMPUTATION: {missingvals_town_after_imputation}")
missingvals_county_after_imputation = Processed_Data_VOA['County'].isnull().sum()
print(f"NUMBER OF MISSING VALUES IN 'County' AFTER IMPUTATION: {missingvals_county_after_imputation}")
missingvals_originaltodate_after_imputation = Processed_Data_VOA['Original_ToDate'].isnull().sum()
print(f"NUMBER OF MISSING VALUES IN 'Original_ToDate' AFTER IMPUTATION: {missingvals_originaltodate_after_imputation}")
NUMBER OF MISSING VALUES IN 'FirmName' AFTER IMPUTATION: 0 NUMBER OF MISSING VALUES IN 'StreetNameNum' AFTER IMPUTATION: 0 NUMBER OF MISSING VALUES IN 'Address3' AFTER IMPUTATION: 0 NUMBER OF MISSING VALUES IN 'Street' AFTER IMPUTATION: 0 NUMBER OF MISSING VALUES IN 'Town' AFTER IMPUTATION: 0 NUMBER OF MISSING VALUES IN 'County' AFTER IMPUTATION: 0 NUMBER OF MISSING VALUES IN 'Original_ToDate' AFTER IMPUTATION: 0
CHECK FOR EMPTY COLUMNS WITH NO DATA INFORMATION IN THE DATASET:
#Check for empty columns
empty_columns_check = Processed_Data_VOA.columns[Processed_Data_VOA.isnull().all()]
if len(empty_columns_check) > 0:
print(f"The following columns in Processed_Data_VOA have no input or are empty: {empty_columns_check}")
else:
print("There are no columns with no input or empty values.")
The following columns in Processed_Data_VOA have no input or are empty: Index(['Address1'], dtype='object')
IMPUTATION METHOD FOR THE EMPTY COLUMN TO RETAIN THE DATATYPE OF 'Address1' :
#Check number of missing values in 'Address1'
missing_count = Processed_Data_VOA['Address1'].isnull().sum()
print(f"NUMBER OF MISSING VALUES IN 'Address1': {missing_count}")
#Impute Address1 with a default value
Processed_Data_VOA.loc[:, 'Address1'] = Processed_Data_VOA['Address1'].fillna(-1)
#Verify if missing values have been filled
missing_count_after_imputation = Processed_Data_VOA['Address1'].isnull().sum()
print(f"NUMBER OF MISSING VALUES IN 'Address1' AFTER IMPUTATION: {missing_count_after_imputation}")
#Verify the data type is retained after imputation
address1_dtype_after_imputation = Processed_Data_VOA['Address1'].dtype
print(f"DATA TYPE OF 'Address1' AFTER IMPUTATION: {address1_dtype_after_imputation}")
NUMBER OF MISSING VALUES IN 'Address1': 96158 NUMBER OF MISSING VALUES IN 'Address1' AFTER IMPUTATION: 0 DATA TYPE OF 'Address1' AFTER IMPUTATION: float64
STATISTICAL SUMMARY ANALYSIS
The Processed_Data_VOA dataset is examined for missing values, and appropriate imputation methods are applied. Each column's purpose is defined in a data dictionary, and a comprehensive overview of the data types and the presence of missing values are identified. The imputation methods used for columns with missing values ensured that the dataset retained no missing value, and the data types were preserved accurately for data integrity purposes to ensure a clean dataset for further analysis.
DATA DICTIONARY: column names are displayed and used to create a data dictionary displaying descriptive informatioaboutof the columns in the Processed_DAta_VOA dataset.
RecordType: Indicates the type of record, such as assessment or billing.
AssessmentReference: A unique reference number assigned to each assessment.
UARN: Unique Assessment Reference Number for each property.
BillingAuthCode: Code representing the billing authority.
FirmName: Name of the firm or occupier of the property.
StreetNameNum: Street name and number of the property.
Address1: Additional address information.
Address3: Additional address information.
Street: Street name where the property is located.
Town: Town where the property is located.
PostDistrict: Postal district of the property.
Local_Authority: Name of the local authority responsible for the property.
County: County where the property is located.
Postcode: Postal code of the property.
SchemeRef: Scheme reference number.
PriDescText: Primary description text of the property.
Area: Area of the property.
AreaSqM: Area of the property in square meters.
AreaSqF: Area of the property in square feet.
£/SqF: Value per square foot of the property.
£/Sqm: Value per square meter of the property.
SubTotal: Subtotal value of the property.
TotalVal: Total value of the property.
AdoptedRV: Adopted rateable value of the property.
ListYear: Year of the list in which the property is included.
BAName: Name of the billing authority.
BARef: Reference number for the billing authority.
VORef: Valuation office reference number.
FromDate: Start date of the assessment.
ToDate: End date of the assessment.
SCATCode: Standard classification of assessment code types of the property.
ECC Cat: Essex county council category of the property.
Unit: Unit of measurement used for the property.
UnAdjPrice: Unadjusted price of the property.
2010: Indicator for the year 2010.
2011: Indicator for the year 2011.
2012: Indicator for the year 2012.
2013: Indicator for the year 2013.
2014: Indicator for the year 2014.
2015: Indicator for the year 2015.
2016: Indicator for the year 2016.
2017: Indicator for the year 2017.
2018: Indicator for the year 2018.
2019: Indicator for the year 2019.
2020: Indicator for the year 2020.
2021: Indicator for the year 2021.
2022: Indicator for the year 2022.
2023: Indicator for the year 2023.
2024: Indicator for the year 2024.
2025: Indicator for the year 2025.
AreaCombined_Group: Grouping based on the combined area of the property.
RateValue: Rateable value of the property.
RV Quartile: Quartile of the rateable value.
Max Value Quartiles: Maximum value quartiles.
Original_FromDate: Original start date of the assessment.
Original_ToDate: Original end date of the assessment.
FromDate2: Secondary start date of the assessment.
PERCENTAGE OF MISSING VALUES: identified in columns of the Dataset.
The percentage of missing values in individual columns is as follows:
FirmName: 13.87%
StreetNameNum: 0.17%
Address1: 100%
Address3: 70.75%
Street: 1.02%
Town: 62.77%
County: 29.28%
Original_ToDate: 86.72%
COLUMNS WITHOUT MISSING VALUES AND COUNTS OF COLUMNS WITH MISSING VALUES.
The following columns have no missing values:
RecordType
AssessmentReference
UARN
BillingAuthCode
PostDistrict
Local_Authority
Postcode
SchemeRef
PriDescText
Area
AreaSqM
AreaSqF
£/SqF
£/Sqm
SubTotal
TotalVal
AdoptedRV
ListYear
BAName
BARef
VORef
FromDate
ToDate
SCATCode
ECC Cat
Unit
UnAdjPrice
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
AreaCombined_Group
RateValue
RV Quartile
Max Value Quartiles
Original_FromDate
FromDate2
COLUMNS WITH MISSING VALUES AND THEIR COUNTS: columns with missing values and their respective percentages are:
FirmName: 13.87%
StreetNameNum: 0.17%
Address1: 100%
Address3: 70.75%
Street: 1.02%
Town: 62.77%
County: 29.28%
Original_ToDate: 86and .72%
IMPUTATION METHODS FOR COLUMNS WITH MISSING VALUES IN THE DATASET:
Impute Fie mode (most frequent value).
Convert StreetName Num to numeric type then impute with the median value.
Impute Address3 with "Unknown".
Impute Street, Town, County with the mode (most frequent value).
Impute Original_ToDate with FromDate plus a duration of one year.
CHECK FOR EMPTY COLUMNS IN THE DATASET: For Address1, which is identified as an empty column, impute with a defa lt value to retain its data type.
DATA TYPE OF COLUMNS: the data types of columns are checked to ensure the correct imputation method is applied and data structure retain to ensure data integrity.
Confirmation of Successful Imputation for columns with missing values: after applying the imputation methods, the columns FirmName, StreetNameNum, Address3, Street, Town, County, and Original_ToDate have no missing values.
DATA VISUALIZATIONS
INTERACTIVE DATA VISUALIZATION OF THE DISTRIBUTION OF INDUSTRIES IN PROCESSED_DATA_VOA:
#Group industries to better understand the distribution of businesses in Processed_Data_VOA
Industry_counts=Processed_Data_VOA['PriDescText'].value_counts().reset_index()
Industry_counts.columns=['Industry', 'Count']
#Interactive pie chart to display general fields of businesses in the dataset
Industry_PieChart=px.pie(
Industry_counts,
values='Count',
names='Industry',
title='INTERACTIVE PIE CHART DISTRIBUTION OF INDUSTRIES IN PROCESSED_DATA_VOA',
hover_data=['Count'],
labels={'Industry': 'Industry'}
)
#update trace to display percentage and label inside pie slices
Industry_PieChart.update_traces(textposition='inside', textinfo='percent+label')
#Display the PieChart
Industry_PieChart.show()
#Save the plot in the working folder
#Use write_image for saving Plotly figures
Industry_PieChart.write_image('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\INTERACTIVE_PIE_CHART_DISTRIBUTION_OF_INDUSTRIES.png')
KEY INSIGHTS AND ANALYSIS ON THE INTERACTIVE PIE CHART DISTRIBUTION OF INDUSTRIES OCCUPYING BUSINESS ACCOMODATIONS IN THE PROCESSED_DATA_VOA DATASET
The interactive pie chart provides a visual representation of the distribution of various industries within the Processed_Data_VOA dataset, displaying the percentage of each industry type with relative size of each slice that indicates the proportion of industries in the dataset.
OBSERVATIONS
1.DOMINANCE OF OFFICE SPACES:
Analysis: The interactive pie chart displays "Offices and Premises" as the category that represents the largest portion of business accomodations in the dataset with a proportion of 35.4% which indicates that administrative and commercial activities are highly prevalent in the dataset.
Key Insight: The significant focus on office-related infrastructure suggests that this sector plays a crucial role in occupying business accomodations in the dataset, potentially reflecting a broader trend within the region or market under analysis.
2.STRONG INDUSTRIAL PRESENCE:
Analysis: The second and third largest category of industries occupying business accomodations in the dataset as displayed by the interactive pie chart are "Workshop and Premises"(25%) and "Warehouse and Premises"(17.5%) which makes up a considerable portion of the dataset and indicates the importance of industrial activities, such as manufacturing, repair, and storage, within the Processed_Data_VOA dataset.
Key Insights: "Workshop and Premises" (25%) which accounts for a quarter of the total data are spaces typically used for manufacturing, repair or maintenance, suggesting a strong presence of industries occupying business accomodations in the dataset.
"Warehouse and Premises" (17.5%) which are essential business accomodations used for storage and logistic distributions, indicates a significant portion of the presence of retail activities in the dataset.
"Store and Premises" (8.4%) are business accomodations that refer to retail spaces and activities, indicating a small but considerable portion of its presence in the dataset.
The substantial representation of these industrial sectors highlights their critical role in the economy or region being studied, emphasizing the need for adequate infrastructure and support services for these industries.
3.SKEWED DISTRIBUTION:
Analysis: The distribution of industries occupying business accomodations in the Processed_Data_VOA dataset is heavily skewed, with major industry categories and a few minor categories that occupy less than 5% presence in the dataset such as "Land Used for Storage and Premises", "Factory and Premises" and "Business Unit and Premises" each having minimal representation as displayed in the interactive pie chart. These categories account for over 77.9% of the total which makes up more than three_quarters of the entire dataset.
Key Insight: This skewness suggests that the dataset or the region it represents is concentrated in a limited number of sectors occupying business accomodations which could indicate a lack of diversification and might be a risk factor in economic planning or investment.
4.DIVERSITY OF INDUSTRIES:
The interactive pie chart reflects a diversity of industry types in the Processed_Data_VOA dataset despite the dominace of a few categories of industries occupying larger proportions of busines accomodations and the representation of other categories of industries occupying smaller proportions of business accomodations.
5.POTENTIAL FOCUS AREA:
The prominence of office spaces as displayed by the pie chart suggests a dataset focused on commercial or business activities, while the substantial share of workshops and warehouses implies significant industrial and logistic activities.
6.POTENTIAL APPLICATIONS: Urban Planning and Zoning: Understanding the distribution of industries occupying business accomodations can inform city planners on how to allocate resources or zones for different types of activities.
Investment and Development: City, urban planners and investors might focus on the dominant categories like office, workshop and warehouse spaces, given their larger presence and percentages in the dataset as priority areas indicating potentially higher demand or availability of these business accomodations.
The data can aid urban and city planners to prioritize infrastructures that supports office and industrial spaces to ensure that the dominant sectors as displayed in the dataset are well utilized for sustainable growth of future urban development and investment strategies.
The data can also guide investors on industries that can be profitable and thrive for economic growth and expansion.
7.INFRASTRUCTURE AND SERVICES:
The data can guide service providers in infrastructure development, such as transportation and logistic networks or utility services, to cater to the predominant industries and understanding the distribution of industries in the data can help to tailor major services to the dominant sectors as displayed by the interactive pie chart, to offer targeted solutions for specific needs of these industries occupying business accomodations.
CONCLUSION: The interactive pie chart provides a clear and insightful summary of the distribution OF INDUSTRIES within the dataset, highlighting key areas of concentration. The significant dominance of office, workshop, and warehouse premises suggests that these are the primary focus areas within the dataset which could have major implications for various sectors, including real estate, urban planning, and economic development.
This analysis provides a comprehensive view of the Processed_Data_VOA dataset, highlighting key trends and insights that can inform decision making in various fields of industrial businesses from urban planning to investment strategies.
#Interactive histogram to visualize the distribution of businesses located geographically
Cities_Histogram=px.histogram(
Processed_Data_VOA,
x='Local_Authority',
title='INTERACTIVE HISTOGRAM FOR THE DISTRIBUTION OF COMPANIES ACROSS CITIES IN PROCESSED_DATA_VOA',
labels={'Local_Authority': 'City', 'count' : 'Number of Companies'},
text_auto=True,
color='Local_Authority'
)
#Update layout for better aesthetics and readability
Cities_Histogram.update_layout(xaxis_title='City', yaxis_title='Number of Companies', bargap=0.2)
#Display the histogram
Cities_Histogram.show()
#Save the plot in the working folder
#Use write_image for saving Plotly figures
Cities_Histogram.write_image('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\INTERACTIVE_HISTOGRAM_FOR_THE_DISTRIBUTION_OF_COMPANIES_ACROSS_CITIES.png')
KEY INSIGHTS AND ANALYSIS ON THE INTERACTIVE HISTOGRAM FOR DISTRIBUTION OF COMPANIES ACROSS CITIES OCCUPYING BUSINESS ACCOMODATIONS IN THE PROCESSED_DATA_VOA DATASET
OBSERVATIONS:
1.HIGH CONCENTRATION OF COMPANIES OCCUPYING BUSINESS ACCOMODATIONS IN SPECIFIC CITIES
Analysis: The interactive histogram reveals cities like Colchester (9,488 companies), Chelmsford (9,546 companies), and Southend-On-Sea (9,021 companies) having the highest number of companies occupying business accomodations in the dataset compared to other cities. These three cities collectively dominate the dataset in terms of company distribution.
Key Insight: The high concentration of companies in these cities suggests they are central business hubs within the region and this could be due to supporting factors like better infrastructure, access to markets, or favorable business environments.
3.MODERATE DISTRIBUTION IN MID-TIER CITIES
Analysis: Cities like Epping Forest (8,397 companies), Uttlesford (9,653 companies), and Basildon (7,120 companies) show a moderate number of companies occupying business accomodations in the dataset. These cities are significant but not as dominant as the major cities like Colchester, Chelmsford, and Southend-On-Sea.
Key Insight: The moderate distribution of business accomodations as displayed by the interactive histogram, indicates that these cities are emerging or secondary business hubs and they are likely to provide a balanced environment that supports both business activities and residential needs, making them attractive to a variety of Lompanies.
4.MOWER BUSINESS DENSITY IN OTHER CITIES DISPLAYED IN THE DATA
Analysis: Cities displayed by the interactive plot such as Castle Point (3,007 companies), Brentwood (4,326 companies), and Maldon (4,379 companies) have a relatively lower number of companies ocupying business accomodations, suggesting that these areas are less commercially dense compared to the top-tier cities in the dataset.
Key Insight: The lower density of businesses could indicate either a smaller market size or possibly limited infrastructure and resources that can support a large number of companies with business accomodations in these cities. These areas might benefit from targeted economic development initiatives to attract more businesses to take up more accomodations in these regions.
5.VARIABILITY ACROSS CITIES.
Analysis: The variability in the number of companies across different cities occupying business accomodations is notable, with some cities having nearly three times as many companies as others. This disparity as visualized in the data highlights the uneven economic development and the concentration of business activities in specific areas.
Key Insight: The variability suggests the presence of regional disparities in economic activities. Policymakers and economic planners might consider strategies to encourage more balanced economic development across the region to reduce disparities and ensure more equitable growth to enable more companies take up business accomodations in these cities.
7.IMPLICATONS FOR URBAN PLANNING AND INVESTMENT
Analysis: The distribution of companies in the dataset that occupy business accomodations across these cities can inform urban planning and investment decisions. Cities with high concentrations of companies might face challenges related to congestion, high property costs, and competition for resources, while cities with lower concentrations might offer untapped opportunities.
Key Insight: Urban planners could focus on improving infrastructure and resources in mid- and lower-tier cities to attract more companies to occupy business accomodations and balance the regional economic growth. Investors might find opportunities in these underrepresented areas, especially in sectors that are less saturated by businesses in the top-tier cities.
8.APPLICATIONS
For economic planners, this data suggests that focusing on infrastructure and business-friendly policies in mid- and lower-tier cities could foster more balanced regional development.
For investors, cities with moderate to lower business densities might offer growth opportunities, especially in emerging sectors or markets.
For businesses that are occupying these accomodations, understanding where companies are concentrated can help in strategic decision-making, such as choosing locations for new branches or targeting specific markets.
This analysis provides a comprehensive view of the business distribution across cities, highlighting key trends and offering insights that can inform various stakeholders, including policymakers, investors, and business leaders for profitable economic decison making processes.or profitable economic decison making processes.
#Histogram to visualize the distribution of businesses across postcodes in the local authority
##group by 'PostDistrict' to get the count of companies in each postcode
Postcode_counts=Processed_Data_VOA['PostDistrict'].value_counts().reset_index()
Postcode_counts.columns=['PostDistrict', 'CompanyCount']
#Interactive plotting
PostDist_Histogram=px.histogram(
Postcode_counts,
x='PostDistrict',
y='CompanyCount',
color='PostDistrict',
title='INTERACTIVE HISTOGRAM FOR THE DISTRIBUTION OF COMPANIES ACROSS POST DISTRICTS IN PROCESSED_DATA_VOA',
labels={'PostDistrict': 'PostDistrict', 'CompanyCount': 'Number of Companies'},
template='plotly_white'
)
#Update layout for better aesthetics and readability
PostDist_Histogram.update_layout(
xaxis={'categoryorder': 'total descending'},
xaxis_title='Post Districts of business locations',
yaxis_title='Number of Companies',
bargap=0.2,
showlegend=False
)
#Show the histogram
PostDist_Histogram.show()
#Save the plot in the working folder
#Use write_image for saving Plotly figures
PostDist_Histogram.write_image('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\INTERACTIVE HISTOGRAM FOR THE DISTRIBUTION OF COMPANIES ACROSS POST DISTRICTS.png')
STATISTICAL SUMMARY ANALYSIS FOR DATA VISUALIZATIONS OF THE DISTRIBUTION OF INDUSTRIES IN PROCESSED_DATA_VOA DATASET:
The descriptive and interactive visualizations provide a comprehensive view of the distribution of industries and companies across different geographical areas in the Processed_Data_VOA dataset which are crucial for identifying business hotspots and understanding the overall business landscape within the dataset.
The interactive pie chart offers insights into the proportion of businesses in various industries, while the histograms illustrate the distribution of companies across cities and post-districts.
write_image is used to save and display interactive plots in the working folder.
INTERACTIVE PIE CHART FOR INDUSTRY DISTRIBUTION
Grouping Industries:
The dataset's PriDescText column is grouped to understand the distribution of various business fields.
The industry counts are calculated using the value_counts() method.
The resulting data frame, Industry_counts, contains two columns: Industry and Count.
Creating Interactive Pie Chart:
An interactive pie chart is created using Plotly Express (px.pie) to display the distribution of industries based on the count of businesses in each industry.
Update_traces method is used to customize the pie chart to display percentages and labels inside each slice.
Visualization Insights:
The interactive pie chart provides a visual representation of the proportion of businesses in different industries in the Processed_Data_VOA.
Key industries include "Offices And Premises" (35%), "Workshop And Premises" (25%), and "Warehouse And Premises" (15%), among others.
The interactive pie chart allows for detailed insights by hovering over each slice to see exact counts and percentages.
INTERACTIVE HISTOGRAM FOR THE DISTRIBUTION OF COMPANIES ACROSS CITIES
Creating Interactive Histogram for Cities: Visualizing a clear comparative view of the distribution of companies across various cities and highlighting major businesses using the Local_Authority column in the Processed_Data_VOA and Plotly Express tool (px.histogram).
The interactive histogram is customized to improve aesthetics and readability with update_layout method, setting titles, and adjusting the bar gap.
Visualization Insights:
The interactive histogram shows the number of companies in each city.
Cities with the highest number of companies include "Basildon" with 9593 companies and "Chelmsford" with 9548 companies.
INTERACTIVE HISTOGRAM FOR THE DISTRIBUTION OF COMPANIES ACROSS POST DISTRICTS IN PROCESSED_DATA_VOA DATASET.
The dataset is grouped by PostDistrict to get the count of companies in each postcode using Plotly Express tool (px.histogram) to create an interactive and descriptive histogram visualizing the distribution of companies across post districts.
The histogram is customized for better readability and aesthetics, including sorting the x-axis by total count in descending order.
Visualization Insights:
The interactive histogram shows the number of companies in each post district.
Post districts such as "COLCHESTER" and "CHELMSFORD" are highlighted descriptively as key business locations having the highest number of companies in the dataset.
This descriptive and interactive visualization helps in understanding the geographical distribution of businesses in the dataset at a more granular postcode level.
KEY ANALYSIS AND INSIGHTS ON THE INTERACTIVE HISTOGRAM FOR THE DISTRIBUTION OF COMPANIES ACROSS POST DISTRICTS IN PROCESSED_DATA_VOA DATASET
OBSERVATIONS:
1.HIGH CONCENTRATION OF COMPANIES IN SPECIFIC POST DISTRICTS
Analysis: The interactive histogram indicates that Colchester stands out significantly with over 10,000 companies, which is by far the highest concentration among all post-districts in the dataset. Following Colchester, districts such as Basildon, Southend-On-Sea, and Brentwood also show relatively high numbers of companies at much lower levels than Colchester.
Key Insight: Colchester district is a major business hub within the region, attracting many companies, likely due to its favorable business environment, strategic location, and robust infrastructure. The significant difference between Colchester and other districts suggests a high degree of centralization of business activities in this area.
2.MODERATE BUSINESS PRESENCE IN SECONDARY POST DISTRICTS
Analysis: Post-districts such as Wickford, Billericay, and Loughton exhibit a moderate number of companies, suggesting that while they are not as prominent as Colchester or Basildon the first and second leading with a large count of businesses, they still play an important role in the regional business landscape.
Key Insight: These secondary post-districts offer a balance of business-friendly environments without the saturation seen in top-tier districts. They may be attractive for companies seeking lower competition or more affordable operating conditions.
3.LOW BUSINESS DENSITY IN OTHER POST DISTRICTS
Analysis: Post districts such as Royston, Rayne, and Chigwell display a notably lower number of companies. These areas have a much smaller business footprint in the dataset compared to the leading districts, indicating less economic activity or business presence.
Key Insight: The low density of businesses in these post-districts could point to either a lack of infrastructure, limited market size, or other barriers to business growth. These areas might represent opportunities for economic development or targeted investment to stimulate business activity.
4.IMPLICATIONS FOR BUSINESS ACCOMMODATION AND LOCATION DECISIONS
Analysis: The interactive histogram reveals a skewed distribution of companies, with a few districts like Colchester dominating the landscape. This information is crucial for assessing potential market saturation and competition levels for companies considering expansion or relocation.
Key Insight: Companies might consider entering districts with moderate to low business density to capitalize on untapped markets. Conversely, entering highly saturated markets like the Colchester district might require a more competitive strategy but could offer access to a larger customer base.
5.REGIONAL ECONOMIC DISPARITIES
Analysis: The vast differences in the number of companies across post-districts in the dataset highlight regional disparities in economic activity. Areas like Colchester are highly developed in terms of business infrastructure as compared to other districts.
Key Insight: Policymakers could focus on balancing regional development by investing in infrastructure and resources in less-developed districts. This could help in spreading economic activity more evenly across the region to reduce dependency on a few key districts.
6.STRATEGIC PLANNING FOR URBAN DEVELOPMENT
Analysis: The concentration of companies in certain post-districts can guide urban development and planning committees. High-density areas may need more investment in public services, transportation, and infrastructure to support their business populations.
Key Insight: Urban planners should consider these business concentrations when developing long-term strategies for sustainable urban growth. Investments in transportation networks, utilities, and commercial real estate in high-density areas could enhance business efficiency and support continued economic growth.
DATA APPLICATIONS
For investors, identifying post-districts with lower business density can reveal opportunities for growth in less saturated markets.
For urban planners, focusing on infrastructure improvements in high-density areas will be crucial for sustaining business growth and preventing congestion.
For business leaders, strategic location decisions can be informed by understanding the distribution of competitors and potential markets across different post-districts.
This analysis of the interactive histogram, provides a comprehensive understanding of the distribution of companies across various post-districts, highlighting key patterns and offering insights that are valuable for stakeholders such as policymakers, investors, and business leaders.
DATA VISUALIZATION OF THE DISTRIBUTION OF BUSINESSES ACROSS DIFFERENT CATEGORIES IN THE PROCESSED_DATA_VOA DATASET
DATA CATEGORIZATION FUNCTIONS:
#Define categorization functions
def Cat_by_geography(Processed_Data_VOA):
# Group by county
County_group = Processed_Data_VOA.groupby('County')['FirmName'].nunique().reset_index(name='Firm_count')
# Group by local authority
Local_Authority_group = Processed_Data_VOA.groupby('Local_Authority')['FirmName'].nunique().reset_index(name='Firm_count')
return County_group, Local_Authority_group
def Cat_by_business_type(Processed_Data_VOA):
# Group by business type
Business_Type_group = Processed_Data_VOA.groupby('PriDescText')['FirmName'].nunique().reset_index(name='Firm_count')
return Business_Type_group
def Cat_by_area_size(Processed_Data_VOA):
# Group by area combined
Business_AreaSize_group = Processed_Data_VOA.groupby(['AreaCombined_Group', 'PriDescText'])['FirmName'].nunique().reset_index(name='Firm_count')
return Business_AreaSize_group
#Apply categorization functions to Processed_Data_VOA
County_group, Local_Authority_group = Cat_by_geography(Processed_Data_VOA)
Business_Type_group = Cat_by_business_type(Processed_Data_VOA)
Business_AreaSize_group = Cat_by_area_size(Processed_Data_VOA)
INTERACTIVE VISUALIZATIONS:
#Display group category for county
print("CATEGORY OF BUSINESSES BY COUNTY IN PROCESSED_DATA_VOA:")
print(tabulate(County_group, headers='keys', tablefmt='psql'))
#Plot businesses by county using interactive horizontal barchart
counties_in_Processed_Data_VOA = County_group.sort_values(by='Firm_count', ascending=False)
#Create plot
County_Barchart=px.bar(
counties_in_Processed_Data_VOA,
x='Firm_count',
y='County',
orientation='h',
title='HORIZONTAL BAR CHART FOR NUMBER OF UNIQUE BUSINESSES BY COUNTIES IN PROCESSED_DATA_VOA',
labels={'Firm_count': 'Number of Businesses', 'County': 'County'},
template='plotly_white',
color='County'
)
#set layout for interactive plot
County_Barchart.update_layout(
xaxis_title='Number of Businesses',
yaxis_title='County',
xaxis={'categoryorder': 'total descending'},
showlegend=False
)
#Display plot
County_Barchart.show()
#Save the plot in the working folder
#Use write_image for saving Plotly figures
County_Barchart.write_image('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\HORIZONTAL_BAR_CHART_FOR_NUMBER_OF_UNIQUE_BUSINESSES_BY_COUNTIES.png')
CATEGORY OF BUSINESSES BY COUNTY IN PROCESSED_DATA_VOA: +----+----------+--------------+ | | County | Firm_count | |----+----------+--------------| | 0 | ESSEX | 520 | | 1 | HERTS | 14 | | 2 | SUFFOLK | 2 | +----+----------+--------------+
KEY INSIGHTS AND ANALYSIS ON THE INTERACTIVE BAR CHART FOR THE NUMBER OF UNIQUE BUSINESSES BY COUNTIES PROCESSED_DATA_VOA DATASET
1.DOMINANCE OF ESSEX COUNTY IN BUSINESS COUNT
Analysis: The interactive bar chart clearly shows that Essex has an overwhelmingly higher number of unique businesses compared to other counties, with over 500 businesses occupying business accommodations in the dataset. This is significantly higher than the numbers for Herts (Hertfordshire) and Suffolk, which have much fewer businesses.
Key Insight: Essex's dominance in terms of business count in the dataset, suggests that it is a central hub for economic activity in the region. The substantial difference in the number of businesses indicates that Essex may offer more favorable conditions for business operations, such as better infrastructure, access to markets, or a supportive business environment.
2.LIMITED BUSINESS PRESENCE IN HERTFORDSHIRE AND SUFFOLK
Analysis: Herts is visualized to have a noticeably smaller number of businesses compared to Essex, but still significantly more than Suffolk, which has the fewest businesses among the counties represented in the dataset. The difference between Essex and these counties is stark.
Key Insight: The limited presence of businesses in Hertfordshire and Suffolk as visualized by the interactive chart, suggests that these counties may have certain limitations or challenges that prevent them from being as competitive as Essex. These could include factors such as less developed infrastructure, lower population density, or less economic investment.
3.POTENTIAL FOR GROWTH IN HERTFORDSHIRE AND SUFFOLK
Analysis: The relatively low number of businesses in Herts and Suffolk could also indicate untapped potential for business growth. If conditions were improved and more investment directed towards these areas, it could lead to an increase in the number of businesses over time.
Key Insight: There is an opportunity for regional development initiatives to focus on Hertfordshire and Suffolk to stimulate business activity. This could involve enhancing infrastructure, providing business incentives, or developing policies to attract new businesses to these areas.
4.IMPLICATIONS FOR REGIONAL ECONOMIC POLICY
Analysis: The clear disparity in the number of businesses across counties in the dataset suggests that regional economic policies may need to be tailored to address the specific needs of each county. Essex may require policies to manage growth and sustain its business ecosystem, while Herts and Suffolk might benefit from policies aimed at business attraction and retention.
Key Insight: Policymakers should consider developing targeted strategies to support business growth in Hertfordshire and Suffolk. This could involve offering tax incentives, improving transportation links, or investing in technology infrastructure to make these counties more attractive to businesses.
5.STRATEGIC CONSIDERATIONS FOR BUSINESSES LOOKING TO OCCUPY BUSINESS ACCOMMODATIONS IN THESE COUNTIES
Analysis: For businesses looking to expand or enter new markets, the distribution of businesses across these counties as visualized, provides valuable information. Essex may offer a vibrant market but with higher competition, while Herts and Suffolk might offer opportunities with less competition but potentially lower immediate returns.
Key Insight: Companies might strategically choose to enter Herts or Suffolk if they are looking for less saturated markets or if they can offer something unique that meets the needs of these underrepresented areas. Conversely, entering Essex could be beneficial for businesses looking to quickly integrate into a bustling economic environment.
DATA APPLICATIONS:
For policymakers, this data should inform regional development strategies, with a focus on reducing disparities between counties and supporting balanced economic growth.
For investors, identifying potential growth areas in Hertfordshire and Suffolk could present opportunities for early investments that benefit from long-term regional development.
For business leaders, understanding the distribution of businesses can help in making informed decisions about where to locate new operations or expand existing ones.
This analysis of the interactive bar chart of the number of unique businesses by counties in the Processed_Data_VOA dataset, provides a comprehensive overview of the business landscape across Essex, Hertfordshire, and Suffolk, highlighting key patterns, insights, and strategic considerations for various stakeholders.
#Display group category for local authority
print("CATEGORY OF BUSINESSES BY LOCAL AUTHORITY IN PROCESSED_DATA_VOA:")
print(tabulate(Local_Authority_group, headers='keys', tablefmt='psql'))
#Plot businesses by local authority group
local_authorities = [
"Basildon", "Braintree", "Brentwood", "Castle Point", "Chelmsford",
"Colchester", "Epping Forest", "Harlow", "Maldon", "Rochford",
"Southend-On-Sea", "Tendring", "Thurrock", "Uttlesford"
]
firm_counts = [38, 36, 8, 6, 43, 23, 119, 87, 22, 23, 24, 25, 57, 45]
#Create a dataframe for plotting
local_authorities_df = pd.DataFrame({
"Local Authority": local_authorities,
"Firm Count": firm_counts
})
#Sort dataframe by Firm Count in descending order for better visualization
local_authorities_df = local_authorities_df.sort_values(by="Firm Count", ascending=False)
#Create interactive bar chart
local_authorities_plot = px.bar(local_authorities_df, x="Firm Count", y="Local Authority", orientation='h',
title='HORIZONTAL BAR CHART FOR CATEGORY OF BUSINESSES BY LOCAL AUTHORITY IN PROCESSED_DATA_VOA',
labels={'Firm Count': 'Firm Count', 'Local Authority': 'Local Authority'},
text='Firm Count', #display firm count on each bar
height=600,
color='Firm Count', #color bars based on Firm Count
color_continuous_scale='viridis' #color scale
)
#Customize layout
local_authorities_plot.update_layout(
xaxis_title="Firm Count",
yaxis_title="Local Authority",
yaxis={'categoryorder':'total ascending'}, #sort by total in ascending order
margin=dict(l=100, r=50, t=50, b=50) #adjust margins for better readability
)
#Display interactive plot
local_authorities_plot.show()
#Save the plot in the working folder
#Use write_image for saving plotly figures
local_authorities_plot.write_image('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\HORIZONTAL_BAR_CHART_FOR_CATEGORY_OF_BUSINESSES_BY_LOCAL_AUTHORITY.png')
CATEGORY OF BUSINESSES BY LOCAL AUTHORITY IN PROCESSED_DATA_VOA: +----+-------------------+--------------+ | | Local_Authority | Firm_count | |----+-------------------+--------------| | 0 | Basildon | 38 | | 1 | Braintree | 36 | | 2 | Brentwood | 8 | | 3 | Castle Point | 6 | | 4 | Chelmsford | 43 | | 5 | Colchester | 23 | | 6 | Epping Forest | 119 | | 7 | Harlow | 87 | | 8 | Maldon | 22 | | 9 | Rochford | 23 | | 10 | Southend-On-Sea | 24 | | 11 | Tendring | 25 | | 12 | Thurrock | 57 | | 13 | Uttlesford | 45 | +----+-------------------+--------------+
KEY INSIGHTS AND ANALYSIS OF THE HORIZONTAL BAR CHART FOR CATEGORY OF BUSINESSES BY LOCAL AUTHORITY IN THE PROCESSED_DATA_VOA DATASET
1.EPPING FOREST LEADING IN BUSINESS CATEGORIES
Analysis: Epping Forest stands out as the local authority with the highest number of business categories in the dataset, totaling 119 firms occupying business accommodation. This figure is significantly higher than the other local authorities.
Key Insight: Epping Forest's dominance in the dataset suggests it is a highly attractive area for businesses across multiple categories. This could be due to favorable business conditions, such as a supportive local government, accessible infrastructure, or proximity to key markets.
2.SIGNIFICANT PRESENCE IN HARLOW AND THURROCK
Analysis: Harlow and Thurrock are also prominent in the business landscape, with 87 and 57 firms respectively but noticeably lower firm counts than Epping Forest. They form the second and third largest groups of businesses by category of local authority in the dataset.
Key Insight: The substantial number of businesses in Harlow and Thurrock indicates these areas are important economic centers within the region. These local authorities likely have specific attributes or policies that attract particular business categories to occupy business accommodations.
3.MODERATE BUSINESS ACTIVITY IN MID-LEVEL LOCAL AUTHORITIES
Analysis: Local authorities in the dataset such as Uttlesford, Chelmsford, and Basildon show moderate business activity, with firm counts ranging from 38 to 45. These areas have a balanced business ecosystem but do not dominate any particular sector.
Key Insight: These mid-level local authorities may provide stable environments for certain business categories but may face competition from neighboring areas with higher business counts and they could also benefit from targeted initiatives to enhance their attractiveness to specific industries.
4.LOWER BUSINESS CATEGORY REPRESENTATION IN CERTAIN AREAS
Analysis: On the lower end, Brentwood and Castle Point have the fewest business categories, with 8 and 6 firms respectively. This indicates a limited range of business activities compared to other local authorities in the dataset.
Key Insight: The low business representation in these areas could be due to a variety of factors, including less favorable economic conditions, smaller populations, or limited infrastructure. These local authorities might need strategic interventions to diversify and increase their business categories.
5.IMPLICATIONS FOR ECONOMIC DEVELOPMENT
Analysis: The disparity in the number of business categories across different local authorities in the dataset, suggests a need for tailored economic development strategies. Local authorities with lower business counts might focus on creating incentives and improving infrastructure to attract more firms to occupy business accommodations.
Key Insight: Economic planners and policymakers should consider these disparities when designing regional development policies. There may be opportunities to leverage the strengths of local authorities like Epping Forest while also addressing the weaknesses in local authorities like Brentwood and Castle Point.
7.STRATEGIC CONSIDERATION FOR BUSINESSES
Analysis: Businesses considering expansion or relocation can use this information on the dataset to identify local authorities that align with their industry needs. For example, Epping Forest might be attractive for companies seeking a dynamic and diverse business environment, while less crowded areas like Castle Point could appeal to businesses looking for less competition.
Key Insight: Understanding the distribution of business categories by local authorities can help companies make informed decisions about where to establish or expand their operations, depending on their strategic objectives.
DATA APPLICATIONS:
For policymakers, this data should guide local authorities in enhancing their business environments to attract more categories of firms to occupy business accommodations, particularly in underrepresented local authorities.
For investors, identifying local authorities with a high concentration of businesses could indicate promising areas for investment, while areas with fewer businesses might offer opportunities for early-stage investments with long-term potential.
For business leaders, the analysis provides a roadmap for businesses looking to expand within the local authorities region, highlighting areas of high activity and potential growth spots.
This analysis provides a comprehensive overview of the distribution of business categories occupying business accommodations across various local authorities in the Processed_Data_VOA dataset, offering insights and strategic considerations for different stakeholders.
#Display group category for business types
print("CATEGORY OF BUSINESS TYPES AND ACCOMODATIONS IN PROCESSED_DATA_VOA:")
print(tabulate(Business_Type_group, headers='keys', tablefmt='psql'))
CATEGORY OF BUSINESS TYPES AND ACCOMODATIONS IN PROCESSED_DATA_VOA: +-----+--------------------------------------------------------+--------------+ | | PriDescText | Firm_count | |-----+--------------------------------------------------------+--------------| | 0 | 0ffices And Premises | 1 | | 1 | Academy, Offices And Premises | 1 | | 2 | Acting School | 1 | | 3 | Agri. Digger Driving/Training Land | 1 | | 4 | Aircraft Hangar & Premises | 1 | | 5 | Aircraft Hangar And Premises | 1 | | 6 | Airfield Support Centre | 1 | | 7 | Amateur Boxing Club And Premises | 1 | | 8 | Animal Exercise Land | 1 | | 9 | Archive Museum And Premises | 1 | | 10 | Art Gallery & Premises | 1 | | 11 | Art Gallery And Premises | 1 | | 12 | Art Studio & Premises | 1 | | 13 | Art Studio And Classroom Premises | 1 | | 14 | Art Studio And Premises | 1 | | 15 | Artists Studio | 1 | | 16 | Auction House And Premises | 1 | | 17 | Bakery And Premises | 1 | | 18 | Beauty Clinc | 1 | | 19 | Beauty Pod And Premises | 1 | | 20 | Beauty Room & Premises | 1 | | 21 | Beauty Room And Premises | 1 | | 22 | Beauty Salon | 1 | | 23 | Beauty Salon & Premises | 1 | | 24 | Beauty Salon And Premises | 1 | | 25 | Beauty Salon, Office & Premises | 1 | | 26 | Beauty Treatment Room | 1 | | 27 | Beauty Treatment Room And Premises | 1 | | 28 | Beauty Treatment Rooms And Premises | 1 | | 29 | Beauty Treatments Room | 1 | | 30 | Bonded Store | 1 | | 31 | Boxing Gym And Premises | 1 | | 32 | Brewery And Premises | 1 | | 33 | Builders Compound And Premises | 1 | | 34 | Builders Merchant And Premises | 2 | | 35 | Builders Merchants And Premises | 1 | | 36 | Builders Merchants Yard & Premises | 1 | | 37 | Builders Yard | 1 | | 38 | Builders Yard & Premises | 1 | | 39 | Builders Yard And Premises | 1 | | 40 | Building Under Reconconstruction | 1 | | 41 | Building Under Reconstruction | 1 | | 42 | Building Under Reconstuction | 1 | | 43 | Building Undergoing Reconstruction | 1 | | 44 | Building Undergoing Works | 1 | | 45 | Building Works | 1 | | 46 | Buildings In Disrepair, Land, Car Parking And Premises | 1 | | 47 | Business Centre And Premises | 1 | | 48 | Business Unit And Premise | 1 | | 49 | Business Unit And Premises | 1 | | 50 | Business Unit, Store And Premises | 1 | | 51 | Business Unit, Workshop And Premises | 1 | | 52 | Butchery And Premises | 1 | | 53 | Cafe | 1 | | 54 | Cafe And Premises | 1 | | 55 | Canine Creche And Premises | 1 | | 56 | Canine Fitness Centre And Premises | 1 | | 57 | Canopy And Premises | 1 | | 58 | Canteen And Premises | 1 | | 59 | Car Hall Offices And Premises | 1 | | 60 | Car Hire Office And Premises | 1 | | 61 | Car Park And Premises | 1 | | 62 | Car Park, Office And Premises | 1 | | 63 | Car Sales And Premises | 1 | | 64 | Car Sales Centre And Premises | 1 | | 65 | Car Sales Warehouse & Premises | 1 | | 66 | Car Showroom And Premises | 1 | | 67 | Car Store, Office And Premises | 1 | | 68 | Car Store,Office & Premises | 1 | | 69 | Car Wash And Premises | 1 | | 70 | Caravan And Premises | 1 | | 71 | Carhall, Offices And Premises | 1 | | 72 | Catering Cabin | 1 | | 73 | Catering Kitchen And Premises | 1 | | 74 | Catering Unit And Premises | 1 | | 75 | Chapel Of Rest, Garages And Premises | 1 | | 76 | Childrens Adventure Centre And Premises | 1 | | 77 | Childrens Centre And Premises | 1 | | 78 | Childrens Play Barn And Premises | 1 | | 79 | Childrens Play Centre And Premises | 1 | | 80 | Chiropody Clinic And Premises | 1 | | 81 | Chiropratic Clinic And Premises | 1 | | 82 | Classroom And Premises | 1 | | 83 | Clinc And Premises | 1 | | 84 | Clinic And Premises | 1 | | 85 | Clinic Room | 1 | | 86 | Club And Premises | 1 | | 87 | Club House And Premises | 1 | | 88 | Coffee Bar And Premises | 1 | | 89 | Cold Store And Premises | 1 | | 90 | Commercial Yard And Premises | 1 | | 91 | Communal Meeting Room | 1 | | 92 | Community Cafe, Offices And Premises | 1 | | 93 | Community Centre And Premises | 1 | | 94 | Computer Centre And Premises | 1 | | 95 | Concrete Testing Labroratory & Prems | 1 | | 96 | Concrete Testing, Laboratory And Premises | 1 | | 97 | Container And Premises | 1 | | 98 | Container Storage Site & Premises | 1 | | 99 | Container Store And Premises | 1 | | 100 | Container Stores And Premises | 1 | | 101 | Containers Store And Premises | 1 | | 102 | Containers Stores And Premises | 1 | | 103 | Continuum Care School And Premises | 1 | | 104 | Contractors Accommodation | 1 | | 105 | Contractors Accomodation | 1 | | 106 | Contractors Compound And Premises | 1 | | 107 | Cookery School And Premises | 1 | | 108 | Counselling Centre | 1 | | 109 | Counselling Room And Premises | 1 | | 110 | Craft Distillery And Premises | 1 | | 111 | Cultural Centre And Premises | 1 | | 112 | Cultural Education Centre And Premises | 1 | | 113 | Dance Drama School And Premises | 1 | | 114 | Dance Studio | 1 | | 115 | Dance Studio And Premises | 1 | | 116 | Dance Studios | 1 | | 117 | Day Care Centre And Premises | 1 | | 118 | Delivery/Sorting Office | 1 | | 119 | Demonstration Room And Premises | 1 | | 120 | Dental Clinic And Premises | 1 | | 121 | Dental Laboratory | 1 | | 122 | Dental Laboratory And Premises | 1 | | 123 | Dental Workshop And Premises | 1 | | 124 | Denture Lab And Premises | 1 | | 125 | Depot And Premises | 1 | | 126 | Dinghy Park | 1 | | 127 | Distribution Centre & Premises | 1 | | 128 | Dog Day Care And Premises | 1 | | 129 | Dog Day Care Centre And Premises | 1 | | 130 | Dog Grooming | 1 | | 131 | Dog Grooming And Premises | 1 | | 132 | Dog Grooming Parlour | 1 | | 133 | Dog Grooming Parlour And Premises | 1 | | 134 | Dog Grooming Pod | 1 | | 135 | Dog Grooming Salon | 1 | | 136 | Dog Parlour & Premises | 1 | | 137 | Dog Parlour And Premises | 1 | | 138 | Dog Training School & Premises | 1 | | 139 | Dog Training School And Premises | 1 | | 140 | Drop In Centre And Premises | 1 | | 141 | Education Centre & Premises | 1 | | 142 | End Of Life Vehicle Facility And Premises | 1 | | 143 | Escape Room And Premises | 1 | | 144 | Ex-Cold Store | 1 | | 145 | Factories And Premises | 1 | | 146 | Factory & Premises | 1 | | 147 | Factory And Premises | 44 | | 148 | Factory Office And Premises | 1 | | 149 | Factory Offices And Premises | 1 | | 150 | Factory, Office And Premises | 2 | | 151 | Factory, Offices And Premises | 1 | | 152 | Factory, Stores And Premises | 1 | | 153 | Factory, Workshop And Premises | 1 | | 154 | Family Centre And Premises | 1 | | 155 | Farmshop And Premises (Partially Exempt) | 1 | | 156 | Fitness And Beauty Centre And Premises | 1 | | 157 | Fitness Centre & Premises | 1 | | 158 | Fitness Centre And Premises | 1 | | 159 | Fitness Room | 1 | | 160 | Fitness Studio | 1 | | 161 | Fitness Studio And Premises | 1 | | 162 | Flying School And Premises | 1 | | 163 | Food Processing Unit And Shop | 1 | | 164 | Food Takeaway And Premises | 1 | | 165 | Forge, Store And Premises | 1 | | 166 | Function Room And Premises | 1 | | 167 | Function Rooms | 1 | | 168 | Funeral Directors & Premises | 1 | | 169 | Gallery And Premises | 1 | | 170 | Garage | 1 | | 171 | Garage And Premises | 1 | | 172 | Garages | 1 | | 173 | Garden Centre And Premises | 1 | | 174 | Goalkeeping Academy And Premises | 1 | | 175 | Greenkeepers Store And Premises | 1 | | 176 | Grooming Parlour | 1 | | 177 | Grooming Parlour And Premises | 1 | | 178 | Guest Suite And Premises | 1 | | 179 | Gym | 1 | | 180 | Gym / Fitness Centre | 1 | | 181 | Gym And Premises | 1 | | 182 | Gymnasium | 1 | | 183 | Gymnasium & Premises | 1 | | 184 | Gymnasium And Premises | 1 | | 185 | Hair Dressing Salon And Premises | 1 | | 186 | Hair Salon And Premises | 1 | | 187 | Hairdressing Salon | 1 | | 188 | Hairdressing Salon And Premises | 1 | | 189 | Hairdressing Unit And Premises | 1 | | 190 | Hall And Premises | 1 | | 191 | Hand Car Wash And Premises | 1 | | 192 | Hand Car Wash Site And Premises | 1 | | 193 | Hangar | 1 | | 194 | Hangar & Premises | 1 | | 195 | Hangar And Premises | 1 | | 196 | Hangar Workshops And Premises | 1 | | 197 | Hangar Workshops Offices And Premises | 2 | | 198 | Hangar, Aircraft Stands And Premises | 1 | | 199 | Hangar, Offices And Premises | 1 | | 200 | Hangar,Warehouse And Premises | 1 | | 201 | Hangars, Aircraft Stand And Premises | 1 | | 202 | Hangars, Workshops And Premises | 1 | | 203 | Hanger And Premises | 1 | | 204 | Haulage Depot And Premises | 1 | | 205 | Haulage Yard And Premises | 1 | | 206 | Healthcare Centre And Premises | 1 | | 207 | Healthcare Offices And Premises | 1 | | 208 | Hi-Tech Industrial Premises | 1 | | 209 | Hollistic And Wellbeing Centre And Premises | 1 | | 210 | Hot Food Takeaway And Premises | 1 | | 211 | Hydratherapy Pool And Premises | 1 | | 212 | Hydrotherapy Pool And Premises | 1 | | 213 | Indoor Climbing Centre And Premises | 1 | | 214 | Indoor Trampline Park | 1 | | 215 | Indoor Trampolining Park | 1 | | 216 | Internet Cafe And Premises | 1 | | 217 | Job Centre And Premises | 1 | | 218 | Kiosk | 1 | | 219 | Kiosk And Premises | 1 | | 220 | Kitchen | 1 | | 221 | Kitchen & Premises | 1 | | 222 | Kitchen And Premises | 1 | | 223 | Kitchen Furniture Workshop And Premises | 1 | | 224 | Kitchen, Workshop And Premises | 1 | | 225 | Kitchens And Premises | 1 | | 226 | Knackers Yard,Workshop And Premises | 1 | | 227 | L.P.G Station And Premises | 1 | | 228 | Lab, Factory & Premises | 1 | | 229 | Laboratories And Premises | 1 | | 230 | Laboratory And Premises | 1 | | 231 | Land Used As Car Wash Area And Premises | 1 | | 232 | Land Used As Hand Carwash And Premises | 1 | | 233 | Land Used For Bus Storage And Premises | 1 | | 234 | Land Used For Car Display And Premises | 1 | | 235 | Land Used For Car Parking | 1 | | 236 | Land Used For Parking | 1 | | 237 | Land Used For Storage | 1 | | 238 | Land Used For Storage & Premises | 1 | | 239 | Land Used For Storage And Premises | 58 | | 240 | Land Used For Storage, Caravan Pitches And Premises | 1 | | 241 | Land Used For Storage, Caravans And Premises | 1 | | 242 | Land Used For Storage, Office And Premises | 1 | | 243 | Land Used For Storage, Store And Premises | 1 | | 244 | Land Used For Storage, Warehouse And Premises | 1 | | 245 | Land Used For Storage, Workshop And Premises | 1 | | 246 | Land Used For Van Storage And Premises | 1 | | 247 | Land Used For Vehicle Sales And Premises | 1 | | 248 | Land Workshop And Premises | 1 | | 249 | Land, Offices & Premises | 1 | | 250 | Lecture Room | 1 | | 251 | Leisure Centre And Premises | 1 | | 252 | Loading Bay | 1 | | 253 | Lock Up Garage | 1 | | 254 | Lorry Aprk And Premises | 1 | | 255 | Lorry Compound | 1 | | 256 | Lorry Park & Premises | 1 | | 257 | Lorry Park And Premises | 1 | | 258 | Lorry Parking Area And Premises | 1 | | 259 | Mandir And Premise (Part Exempt) | 1 | | 260 | Marketing Suite | 1 | | 261 | Marketing Suite & Premises | 1 | | 262 | Marketing Suite And Premises | 1 | | 263 | Martial Arts Centre And Premises | 1 | | 264 | Martial Arts Studio And Premises | 1 | | 265 | Massage Spa And Premsies | 1 | | 266 | Meeting Room And Premises | 1 | | 267 | Meeting Rooms And Premises | 1 | | 268 | Mortuary Chapel | 1 | | 269 | Mot Centre And Premises | 1 | | 270 | Museum And Premises | 1 | | 271 | Nail Bar And Premises | 1 | | 272 | Nail Studio And Premises | 1 | | 273 | Newspaper Printing Works And Premises | 1 | | 274 | Nursery And Premises | 1 | | 275 | Observatory And Premises | 1 | | 276 | Office | 1 | | 277 | Office & Premises | 1 | | 278 | Office & Storage Premises | 1 | | 279 | Office And Premises | 2 | | 280 | Office And Storage Premises | 1 | | 281 | Office And Store | 1 | | 282 | Office In Industrial Unit And Premises | 1 | | 283 | Office In Warehouse And Premises | 1 | | 284 | Office Store And Premises | 1 | | 285 | Office Stores And Premises | 1 | | 286 | Office Used As Store | 1 | | 287 | Office Warehouse And Premises | 1 | | 288 | Office Workshop And Premises | 1 | | 289 | Office, Photographic Studio And Premises | 1 | | 290 | Office, Rooms & Premises | 1 | | 291 | Office, Showroom And Premises | 1 | | 292 | Office, Store And Premises | 1 | | 293 | Office, Stores And Premises | 1 | | 294 | Office, Workroom And Premises | 1 | | 295 | Office, Workshop And Premises | 1 | | 296 | Office/Studio And Premises | 1 | | 297 | Office/Workroom And Premises | 1 | | 298 | Offices | 1 | | 299 | Offices Premises | 1 | | 300 | Offices & Premises | 3 | | 301 | Offices An Premises (Beyond Economic Repair) | 1 | | 302 | Offices And Premises | 139 | | 303 | Offices And Premises ( Part Exempt ) | 1 | | 304 | Offices And Premises (Beyond Economic Repair) | 1 | | 305 | Offices And Premises (Part Exempt) | 1 | | 306 | Offices In Warehouse And Premises | 1 | | 307 | Offices Jetty And Premises | 1 | | 308 | Offices Laboratories And Premises | 1 | | 309 | Offices Store And Premises | 1 | | 310 | Offices Used As Stores | 1 | | 311 | Offices Warehouse And Premises | 1 | | 312 | Offices Workroom And Premises | 1 | | 313 | Offices Workshop And Premises | 1 | | 314 | Offices, Car Space And Premises | 2 | | 315 | Offices, Factory And Premises | 1 | | 316 | Offices, Jetty And Premises | 1 | | 317 | Offices, Laboratories And Premises | 1 | | 318 | Offices, Land And Premises | 1 | | 319 | Offices, Office And Premises | 1 | | 320 | Offices, Storage Land And Premises | 1 | | 321 | Offices, Store And Premises | 1 | | 322 | Offices, Stores And Premises | 1 | | 323 | Offices, Warehouse And Premises | 1 | | 324 | Offices, Workshop And Premises | 1 | | 325 | Offices, Workshops And Premises | 1 | | 326 | Offices,Warehouses & Premises | 1 | | 327 | Offices,Workshop And Premises | 1 | | 328 | Offioce And Premises | 1 | | 329 | Open Fronted Store | 1 | | 330 | Ortho/Dental Surgery & Premises | 1 | | 331 | Parenting Centre And Premises | 1 | | 332 | Party Room And Premises | 1 | | 333 | Pet Cemetery | 1 | | 334 | Pet Groomer And Premises | 1 | | 335 | Pet Groomers And Premises | 1 | | 336 | Pet Grooming Parlour | 1 | | 337 | Petrol Filling Station And Premises | 1 | | 338 | Photographic Studio | 1 | | 339 | Photographic Studio And Premises | 1 | | 340 | Photography Studio | 1 | | 341 | Photography Studio And Premises | 1 | | 342 | Physio Room And Premises | 1 | | 343 | Physiotherapy Clinic | 1 | | 344 | Physiotherapy Clinic And Premises | 1 | | 345 | Physiotherapy Studio And Premises | 1 | | 346 | Physiothery Treatment Centre And Premises | 1 | | 347 | Pilates Studio And Premises | 1 | | 348 | Play Centre And Premises | 1 | | 349 | Playcentre And Premises | 1 | | 350 | Podiatry Clinic And Premises | 1 | | 351 | Portable Building | 1 | | 352 | Portable Buildings And Premises | 1 | | 353 | Portacabin | 1 | | 354 | Portacabin And Premises | 1 | | 355 | Portacabins | 1 | | 356 | Portakabin Office | 1 | | 357 | Post Office Sorting Office | 1 | | 358 | Post Office Sorting Office & Premises | 1 | | 359 | Pottery Studio And Premises | 1 | | 360 | Printing Works And Premises | 1 | | 361 | Property Under Reconstruction | 2 | | 362 | Property Undergoing Reconstruction | 1 | | 363 | Pysiotherapy Studio | 1 | | 364 | Pysiotherapy Studio And Premises | 1 | | 365 | Pysiotherapy Treatment Centre And Premises | 1 | | 366 | Rail Freight Terminal And Premises | 1 | | 367 | Reatail Warehouse & Premises | 1 | | 368 | Reception And Meeting Room | 1 | | 369 | Recording Studio And Premises | 1 | | 370 | Recycling Centre And Premises | 1 | | 371 | Research Centre & Premises | 1 | | 372 | Research Centre And Premises | 1 | | 373 | Respite Care Home And Premises | 1 | | 374 | Rest Room And Premises | 1 | | 375 | Restaurant And Premises | 1 | | 376 | Retail And Premises | 1 | | 377 | Retail Unit | 1 | | 378 | Retail Unit And Premises | 1 | | 379 | Retail Warehouse & Premises | 1 | | 380 | Retail Warehouse And Premises | 1 | | 381 | Retail Workshop And Premises | 1 | | 382 | Retail, Workshop And Premises | 1 | | 383 | Royal Mail Sorting Office | 1 | | 384 | Sales & Marketing Suite | 1 | | 385 | Sales And Marketing Suite | 1 | | 386 | Sales Area And Premises | 1 | | 387 | Sales Office | 1 | | 388 | Sales Office And Premises | 1 | | 389 | Salon And Premises | 1 | | 390 | Salvage/Breakers Yard And Premises | 1 | | 391 | Scrapyard And Premises | 1 | | 392 | Sea Cadet Unit And Premises | 1 | | 393 | Security Office And Premises | 1 | | 394 | Self Storage And Premises | 1 | | 395 | Shop | 1 | | 396 | Shop And Premises | 1 | | 397 | Shop Unit | 1 | | 398 | Shop Workshop And Premises | 1 | | 399 | Shorwoom, Office And Premises | 1 | | 400 | Showroom And Premises | 1 | | 401 | Showroom Office And Premises | 1 | | 402 | Showroom Warehouse And Premises | 1 | | 403 | Showroom, Office And Premises | 1 | | 404 | Showroom, Offices And Premises | 1 | | 405 | Showroom, Warehouse And Premises | 1 | | 406 | Showroom, Workshop And Premises | 1 | | 407 | Site Of Former Lorry Park | 1 | | 408 | Site Office & Premises | 1 | | 409 | Site Office And Premises | 1 | | 410 | Small Arena And Premises | 1 | | 411 | Sorage Container | 1 | | 412 | Sorting Centre And Premises | 1 | | 413 | Sorting Office | 2 | | 414 | Sorting Office & Premises | 1 | | 415 | Sorting Office And Premise | 1 | | 416 | Sorting Office And Premises | 3 | | 417 | Sorting Offices And Premises | 1 | | 418 | Sports Injury Clinic And Premises | 1 | | 419 | Sports Therapy Clinic | 1 | | 420 | Storage And Premises | 2 | | 421 | Storage Container | 1 | | 422 | Storage Container And Premises | 1 | | 423 | Storage Container Site | 1 | | 424 | Storage Container Site And Premises | 1 | | 425 | Storage Containers | 1 | | 426 | Storage Containers & Premises | 1 | | 427 | Storage Containers And Premises | 1 | | 428 | Storage Depot & Premises | 1 | | 429 | Storage Depot And Premises | 4 | | 430 | Storage Depot Jetty And Premises | 1 | | 431 | Storage Depot, Office And Premises | 1 | | 432 | Storage Land Store ( In Disrepair) And Premises | 1 | | 433 | Storage Land Workshop And Premises | 1 | | 434 | Storage Unit And Premises | 2 | | 435 | Storage Yard And Premises | 1 | | 436 | Store | 1 | | 437 | Store & Premises | 1 | | 438 | Store (In Disrepair) | 2 | | 439 | Store And Premise | 1 | | 440 | Store And Premises | 46 | | 441 | Store And Premises (In Disrepair) | 1 | | 442 | Store Office And Premises | 1 | | 443 | Store Room And Premises | 1 | | 444 | Store, Car Park And Premises | 1 | | 445 | Store, Land And Premises | 1 | | 446 | Store, Office And Premises | 1 | | 447 | Store, Offices And Premises | 1 | | 448 | Store, Store And Premises | 1 | | 449 | Store, Workshop And Premises | 1 | | 450 | Store,Office And Premises | 1 | | 451 | Storeage Containers And Premises | 1 | | 452 | Stores And Premises | 1 | | 453 | Stores And Premises | 1 | | 454 | Stores Office And Premises | 1 | | 455 | Stores Sales And Premises | 1 | | 456 | Stores Showroom And Premises | 1 | | 457 | Stores Yard And Premises | 1 | | 458 | Stores, Offices And Premises | 1 | | 459 | Stores,Office And Premises | 1 | | 460 | Stores/Land For Storage And Premises | 1 | | 461 | Strongroom And Premises | 1 | | 462 | Studio | 1 | | 463 | Studio & Premises | 1 | | 464 | Studio And Premises | 1 | | 465 | Studio In Warehouse And Premises | 1 | | 466 | Studio Workshop | 1 | | 467 | Studio Workshop And Premises | 1 | | 468 | Stuidio And Premises | 1 | | 469 | Surgery And Premises | 1 | | 470 | Switch Centre | 1 | | 471 | Tanning Studio | 1 | | 472 | Tanning Studio And Premises | 1 | | 473 | Tattoo Parlour | 1 | | 474 | Tattoo Parlour And Premises | 1 | | 475 | Tattoo Studio | 1 | | 476 | Tattoo Studio And Premises | 1 | | 477 | Taxi Office And Premises | 1 | | 478 | Therapy Centre & Premises | 1 | | 479 | Therapy Centre And Premises | 1 | | 480 | Therapy Clinic And Premises | 1 | | 481 | Therapy Room And Premises | 1 | | 482 | Therapy Rooms And Premises | 1 | | 483 | Timber Yard, Stores, Showroom & Premises | 1 | | 484 | Timber Yard,Stores,Showroom & Premises | 1 | | 485 | Tourist Office&Premises | 1 | | 486 | Trader Provided Area | 1 | | 487 | Training Centre And Premises | 1 | | 488 | Training Facility, Office And Premises | 1 | | 489 | Training Room And Premises | 1 | | 490 | Training Room, Kitchen And Premises | 1 | | 491 | Training Rooms & Premises (Partially Exempt) | 1 | | 492 | Training Rooms And Premises | 1 | | 493 | Training Salon And Premises | 1 | | 494 | Trampoline Centre And Premises | 1 | | 495 | Trampoline Park And Premises | 1 | | 496 | Treatment Room | 1 | | 497 | Treatment Room And Premises | 1 | | 498 | Treatment Rooms | 1 | | 499 | Tuition Centre And Premises | 1 | | 500 | Tyre And Exhaust Centre And Premises | 1 | | 501 | Used Car Sales, Workshop And Premises | 1 | | 502 | Van Hire Site And Premises | 1 | | 503 | Vehicle Cleansing Depot And Premises | 1 | | 504 | Vehicle Repair Training Room, Workshop And Premises | 1 | | 505 | Vehicle Repair Workshop | 1 | | 506 | Vehicle Repair Workshop And Premises | 10 | | 507 | Vehicle Repair Workshop Showroom And Premises | 1 | | 508 | Vehicle Repair Workshop, Car Sales And Premises | 1 | | 509 | Vehicle Repair Workshop, Car Showroom And Premises | 1 | | 510 | Vehicle Repair Workshop,Office And Premises | 1 | | 511 | Vehicle Sales And Premises | 1 | | 512 | Vehicle Storage Land | 1 | | 513 | Vehicle Workshop And Premises | 1 | | 514 | Venue, Practice & Meeting Rooms | 1 | | 515 | Warehouse Office And Premises | 1 | | 516 | Warehouse & Premises | 1 | | 517 | Warehouse , Office And Premises | 1 | | 518 | Warehouse And Premises | 84 | | 519 | Warehouse Office And Premises | 1 | | 520 | Warehouse Offices And Premises | 1 | | 521 | Warehouse Sales And Premises | 1 | | 522 | Warehouse Showroom And Premises | 1 | | 523 | Warehouse Used For Retail And Premises | 1 | | 524 | Warehouse, Cold Store And Premises | 1 | | 525 | Warehouse, Land And Premises | 1 | | 526 | Warehouse, Office And Premises | 1 | | 527 | Warehouse, Offices & Premises | 1 | | 528 | Warehouse, Offices And Premises | 1 | | 529 | Warehouse, Sales Area And Premises | 1 | | 530 | Warehouse, Shop And Premises | 1 | | 531 | Warehouse, Showroom And Premises | 1 | | 532 | Warehouse, Stables And Premises | 1 | | 533 | Warehouse, Store And Premises | 2 | | 534 | Warehouse, Workshop And Premises | 1 | | 535 | Warehouse,Car Sales And Premises | 1 | | 536 | Warehouse,Office And Premises | 1 | | 537 | Warehouse,Workshop & Offices And Premises | 1 | | 538 | Warehouses And Premises | 1 | | 539 | Warehouses, Offices And Premises | 1 | | 540 | Waste Transfer Site And Premises | 1 | | 541 | Water Bottling Centre And Premises | 1 | | 542 | Work Unit And Premises | 1 | | 543 | Workroom And Premises | 1 | | 544 | Works And Premises | 1 | | 545 | Works Office, Store And Premises | 1 | | 546 | Works Offices & Premises | 1 | | 547 | Workshop | 1 | | 548 | Workshop & Premises | 1 | | 549 | Workshop & Premises | 1 | | 550 | Workshop And Cookery School | 1 | | 551 | Workshop And Premises | 141 | | 552 | Workshop And Premises (Part Exempt) | 1 | | 553 | Workshop And Prems | 1 | | 554 | Workshop And Sleeping Quarters | 1 | | 555 | Workshop Car Sales And Premises | 1 | | 556 | Workshop Car Sales Site And Premises | 1 | | 557 | Workshop Land And Premises | 1 | | 558 | Workshop Office And Premises | 1 | | 559 | Workshop Offices And Premises | 1 | | 560 | Workshop Shop And Premises | 1 | | 561 | Workshop Showroom And Premises | 1 | | 562 | Workshop Showrooms And Premises | 1 | | 563 | Workshop Showrrom And Premises | 1 | | 564 | Workshop Storage Land And Premises | 1 | | 565 | Workshop Store And Premises | 1 | | 566 | Workshop Stores And Premises | 1 | | 567 | Workshop Studio And Premises | 1 | | 568 | Workshop Stuidio | 1 | | 569 | Workshop Warehouse And Premises | 1 | | 570 | Workshop Yard And Premises | 3 | | 571 | Workshop, Car Sales And Premises | 1 | | 572 | Workshop, Gallery And Premises | 1 | | 573 | Workshop, Land & Premises | 1 | | 574 | Workshop, Land And Premises | 1 | | 575 | Workshop, Office & Premises | 1 | | 576 | Workshop, Office And Premises | 3 | | 577 | Workshop, Offices & Premises | 1 | | 578 | Workshop, Offices And Premises | 3 | | 579 | Workshop, Retail, Store And Premises | 1 | | 580 | Workshop, Shop And Premises | 1 | | 581 | Workshop, Shop, Store And Premises | 1 | | 582 | Workshop, Showroom And Premises | 1 | | 583 | Workshop, Storage Containers And Premises | 1 | | 584 | Workshop, Storage Land & Premises | 1 | | 585 | Workshop, Store And Premises | 1 | | 586 | Workshop, Stores And Premises | 1 | | 587 | Workshop, Warehouse And Premises | 1 | | 588 | Workshop, Workshop And Premises | 1 | | 589 | Workshop, Yard And Premises | 1 | | 590 | Workshop,Offce And Premises | 1 | | 591 | Workshop,Office And Premises | 1 | | 592 | Workshop,Offices And Premises | 1 | | 593 | Workshop,Portacabins And Premises | 1 | | 594 | Workshop,Storage Land And Premises | 1 | | 595 | Workshop/Land And Premises | 1 | | 596 | Workshops And Premises | 1 | | 597 | Workshops And Premises | 1 | | 598 | Workshops And Premises | 2 | | 599 | Workshops Offices And Premises | 2 | | 600 | Workshops, Land And Premises | 1 | | 601 | Workshops, Offices, Equestrian Centre And Premises | 1 | | 602 | Workshops, Stores And Premises | 1 | | 603 | Workspace And Premises | 1 | | 604 | Worm Distribution Store And Premises | 1 | | 605 | Yard Used For Storage And Premises | 1 | | 606 | Yard, Office And Premises | 1 | | 607 | Yoga Centre And Premises | 1 | | 608 | Yoga Studio And Premises | 1 | +-----+--------------------------------------------------------+--------------+
#Select 10 major business accomodations for plotting
#define the data
Property_types = [
"Workshop And Premises",
"Office And Premises",
"Warehouse And Premises",
"Shop And Premises",
"Store And Premises",
"Warehouse",
"Office",
"Shop",
"Store",
"Retail Warehouse And Premises"
]
Property_types_freqs = [141, 139, 84, 46, 46, 44, 36, 34, 31, 27]
#Create a dataframe for plotting
Property_types_data = {"Property Type": Property_types, "Frequency": Property_types_freqs}
Property_types_data_df = pd.DataFrame(Property_types_data)
#Sort dataframe by Frequency in descending order to ensure correct plotting order
Property_types_data_df = Property_types_data_df.sort_values(by="Frequency", ascending=False)
#Create interactive bar chart using Plotly Express
Property_types_plot = px.bar(Property_types_data_df,
x="Frequency",
y="Property Type",
orientation='h', #horizontal orientation
title='BAR CHART DISTRIBUTION OF FREQUENCIES ACROSS BUSINESS PROPERTY TYPES IN PROCESSED_DATA_VOA',
labels={'Frequency': 'Frequency', 'Property Type': 'Property Type'},
text='Frequency',
height=600, #height of the plot
color='Frequency', #color by freqency range
color_continuous_scale='Viridis', #color scale
)
#Customize layout
Property_types_plot.update_traces(textposition='inside') #place frequency label inside bars
Property_types_plot.update_layout(uniformtext_minsize=12, uniformtext_mode='hide') #hide text if it doesn't fit
#Display interactive plot
Property_types_plot.show()
#Save the plot in the working folder
#Use write_image for saving plotly figures
Property_types_plot.write_image('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\BAR CHART DISTRIBUTION OF FREQUENCIES ACROSS BUSINESS PROPERTY TYPES.png')
KEY INSIGHTS AND ANALYSIS FOR THE INTERACTIVE BARCHART DISTRIBUTION OF FREQUENCIES ACROSS BUSINESS PROPERTY TYPES IN THE PROCESSED_DATA_VOA DATASET
1.DOMINANCE OF WORKSHOP AND OFFICE PROPERTIES
Analysis: The interactive bar chart clearly shows that "Workshop and Premises" and "Office and Premises" are the most frequent property types, with counts of 141 and 139, respectively.
Key Insight: This indicates that these property types are the most prevalent across the business landscape in the dataset. The high frequency suggests that there is a substantial demand for both workshops and office spaces, possibly due to the diverse range of business activities that require these types of properties.
2.SIGNIFICANT PRESENCE OF WAREHOUSES
Analysis: "Warehouse and Premises" follows as the third most common property type in the dataset with a frequency of 84.
Key Insight: Warehouses are vital for storage and distribution activities, which are likely supported by the robust commercial infrastructure within the region. The presence of a significant number of warehouses indicates strong industrial and logistical activities in the dataset.
3.MODERATE REPRESENTATION OF RETAIL AND COMMERCIAL PROPERTIES
Analysis: Property types like "Store and Premises," "Shop and Premises," and "Warehouse" have moderate frequencies, ranging from 44 to 46.
Key Insight: These property types represent essential retail and commercial functions, indicating a balanced presence of retail stores and shops in the dataset, and their moderate frequencies suggest a stable, yet not overly dominant, role in the local economy.
4.LOWER FREQUENCY OF SPECIALTY AND NICHE PROPERTIES
Analysis: The "Retail Warehouse and Premises" property type shows the lowest frequency at 27, followed closely by "Store" at 31 and "Shop" at 34.
Key Insight: The lower frequency of these property types might indicate niche markets or less demand for these specific kinds of premises. Retail warehouses, in particular, may cater to specialized industries or specific geographic areas within the region.
5.IMPLICATIONS FOR PROPERTY DEVELOPERS AND INVESTORS
Analysis: The distribution of property types suggests where demand is highest, particularly for workshops, offices, and warehouses in the dataset.
Key Insight: Developers and investors could consider focusing on these high-demand property types to maximize returns. The lower frequency of certain property types like retail warehouses might also present opportunities for growth in under-served markets.
DATA APPLICATIONS:
For business owners, understanding the distribution of property types can help businesses identify where there might be available accommodations that meet their specific needs, particularly in high-demand areas like workshops and offices.
For investors, this analysis provides insights into which property types are most common and potentially most profitable, helping investors to focus on properties that align with market demand.
For urban planners, the data could inform planning decisions, especially in ensuring that there is adequate infrastructure to support the most frequent property types, such as workshops, offices, and warehouses.
This analysis highlights the key trends in the distribution of business property types in the Processed_Data_VOA dataset, providing valuable insights for stakeholders involved in the real estate, investment, and business sectors.
STATISTICAL SUMMARY ANALYSIS: DATA VISUALIZATION OF THE DISTRIBUTION OF BUSINESSES ACROSS DIFFERENT CATEGORIES IN THE PROCESSED_DATA_VOA DATASET USING INTERACTIVE PLOTS.
CATEGORY OF BUSINESSES BY COUNTY IN PROCESSED_DATA_VOA:
Essex can be seen as the primary county for businesses in the dataset, with a particularly high concentration in Epping Forest and Harlow as major business hubs.
The dataset's most common types of business properties are workshops and offices, indicating a frequent occurrence of these kinds of business operations.
The data highlights specific regions and property types that dominate the business environment, providing useful insights for understanding the distribution and categorization of businesses in the area and displaying the presence of workshops and offices highlighting the demand for these types of business accommodations.
The visual analysis of businesses by county shows significant disparities in the concentration of firms across different regions in the dataset with key insights such as:
Essex is displayed as the leading county with a total of 520 unique firm count indicating that Essex is recognised as a major business hub in the dataset.
Hertfordshire(Herts) is seen with a total of 14 firm counts indicating a smaller presence of business activities compared to Essex.
Suffolk has the lowest total of 2 firm count in the dataset compared to Essex and Hertfordshire.
The vast difference in the number of firms between Essex and Hertfordshire / Suffolk underscores Essex's prominence in the business landscape covered by the Processed_Data_VOA dataset.
CATEGORY OF BUSINESSES BY LOCAL AUTHORITY IN PROCESSED_DATA_VOA:
Further analysis visually displays the distribution of businesses across local authorities within the counties in the Processed_Data_VOA dataset, with key insights such as:
Epping Forest is seen as the most business-dense local authority, with 119 firm counts highlighting that Epping Forest as a highly attractive area for business operations.
Harlow is seen to have 87 firm counts indicating a strong business presence in the dataset after Epping Forest.
Thurrock has 57 firm counts following up as the third strong business accommodation in the dataset.
Local authorities with firm counts of business accommodations lower than the top three include Uttlesford with 45 firm counts, Chelmsford with 43 firm counts, Basildon with 38 firm counts, and Braintree with 36 firm counts.
Other local authorities with lower firm counts of businesses in the dataset are Tendring with 25 firm counts, Southend-on-sea with 24 firm counts, Colchester with 23 firm counts, and Rochford with 23 firm counts, each contributing substantially to the local business environment.
On the much lower end, Castle Point and Brentwood have the least number of firms, with 6 and 8 firm counts respectively in the dataset.
These findings highlight the variability in business density within counties, with certain local authorities acting as key business centers in the Processed_Data_VOA data
CATEGORY OF BUSINESS TYPES AND ACCOMMODATIONS IN PROCESSED_DATA_VOA:
This analysis of business types and accommodations provides insights into the frequent forms of business premises in the dataset.
Workshop And Premises are seen as the most common type of business accommodation, with 141 firm counts in the dataset which suggests a high demand for workshop spaces.
Office And Premises follows closely with 139 firm counts, indicating the importance of office spaces in the business ecosystem. Warehouse And Premises with 84 firm counts, Shop And Premises with 46 firm counts, and Store And Premises with 46 firm counts also feature prominently in the dataset reflecting diverse business needs.
other business types with a much lesser appearance in the dataset include Retail Warehouse And Premises with 27 firm counts and Store with 31 firm counts. This distribution indicates that workshops and offices accommodations are essential business premises, likely due to their utility and versatilit
write_image is used to save and display interactive plots in the designated working folder.y in the dataset.
INTERACTIVE VISUALIZATIONS:
Bar Chart Distribution of Frequencies Across Business Property Types: the interactive bar chart visually represents the frequencies of different property types, highlighting the dominance of Workshop And Premises and Office And Premises in the Processed_Data_VOA dataset. This interactive visualization helps to understand the types of business accommodations more prevalent in the dataset.
Horizontal Bar Chart for Category of Businesses by Local Authority: this interactive chart emphasizes the distribution of businesses by local authorities, with Epping Forest and Harlow standing out as major business centers, providing a visual comparison of firm counts across different local authorities which makes it easy to identify business hubs in the dataset.
Horizontal Bar Chart for Number of Unique Businesses by Counties: this interactive chart shows that Essex has a significantly higher number of business accommodations and firm counts as compared to Herts and Suffolk, which underscores Essex's central role in the business landscape.
DATA VISUALIZATION FOR THE AREA SIZE OCCUPIED BY THE BUSINESS ACCOMODATIONS IN THE PROCESSED_DATA_VOA DATASET USING INTERACTIVE AND DESCRIPTIVE PLOTS.
#Display group category for area size of accomodation occupied by different business types
print("CATEGORY OF BUSINESS ACCOMODATIONS BY SIZE OF AREAS OCCUPIED IN PROCESSED_DATA_VOA:")
print(tabulate(Business_AreaSize_group, headers='keys', tablefmt='psql'))
CATEGORY OF BUSINESS ACCOMODATIONS BY SIZE OF AREAS OCCUPIED IN PROCESSED_DATA_VOA: +-----+----------------------------------------------+--------------------------------------------------------+--------------+ | | AreaCombined_Group | PriDescText | Firm_count | |-----+----------------------------------------------+--------------------------------------------------------+--------------| | 0 | 1. <1,500 sq ft (<c.150 sq m) | 0ffices And Premises | 1 | | 1 | 1. <1,500 sq ft (<c.150 sq m) | Airfield Support Centre | 1 | | 2 | 1. <1,500 sq ft (<c.150 sq m) | Archive Museum And Premises | 1 | | 3 | 1. <1,500 sq ft (<c.150 sq m) | Art Gallery & Premises | 1 | | 4 | 1. <1,500 sq ft (<c.150 sq m) | Art Gallery And Premises | 1 | | 5 | 1. <1,500 sq ft (<c.150 sq m) | Art Studio & Premises | 1 | | 6 | 1. <1,500 sq ft (<c.150 sq m) | Art Studio And Classroom Premises | 1 | | 7 | 1. <1,500 sq ft (<c.150 sq m) | Art Studio And Premises | 1 | | 8 | 1. <1,500 sq ft (<c.150 sq m) | Artists Studio | 1 | | 9 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Clinc | 1 | | 10 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Pod And Premises | 1 | | 11 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Room & Premises | 1 | | 12 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Room And Premises | 1 | | 13 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon | 1 | | 14 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon & Premises | 1 | | 15 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon And Premises | 1 | | 16 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon, Office & Premises | 1 | | 17 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Treatment Room | 1 | | 18 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Treatment Room And Premises | 1 | | 19 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Treatment Rooms And Premises | 1 | | 20 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Treatments Room | 1 | | 21 | 1. <1,500 sq ft (<c.150 sq m) | Bonded Store | 1 | | 22 | 1. <1,500 sq ft (<c.150 sq m) | Boxing Gym And Premises | 1 | | 23 | 1. <1,500 sq ft (<c.150 sq m) | Builders Yard | 1 | | 24 | 1. <1,500 sq ft (<c.150 sq m) | Building Under Reconstruction | 1 | | 25 | 1. <1,500 sq ft (<c.150 sq m) | Building Undergoing Reconstruction | 1 | | 26 | 1. <1,500 sq ft (<c.150 sq m) | Building Undergoing Works | 1 | | 27 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | 1 | | 28 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit, Store And Premises | 1 | | 29 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit, Workshop And Premises | 1 | | 30 | 1. <1,500 sq ft (<c.150 sq m) | Cafe | 1 | | 31 | 1. <1,500 sq ft (<c.150 sq m) | Cafe And Premises | 1 | | 32 | 1. <1,500 sq ft (<c.150 sq m) | Canine Fitness Centre And Premises | 1 | | 33 | 1. <1,500 sq ft (<c.150 sq m) | Canopy And Premises | 1 | | 34 | 1. <1,500 sq ft (<c.150 sq m) | Car Hire Office And Premises | 1 | | 35 | 1. <1,500 sq ft (<c.150 sq m) | Car Wash And Premises | 1 | | 36 | 1. <1,500 sq ft (<c.150 sq m) | Caravan And Premises | 1 | | 37 | 1. <1,500 sq ft (<c.150 sq m) | Catering Cabin | 1 | | 38 | 1. <1,500 sq ft (<c.150 sq m) | Catering Kitchen And Premises | 1 | | 39 | 1. <1,500 sq ft (<c.150 sq m) | Catering Unit And Premises | 1 | | 40 | 1. <1,500 sq ft (<c.150 sq m) | Chiropody Clinic And Premises | 1 | | 41 | 1. <1,500 sq ft (<c.150 sq m) | Chiropratic Clinic And Premises | 1 | | 42 | 1. <1,500 sq ft (<c.150 sq m) | Classroom And Premises | 1 | | 43 | 1. <1,500 sq ft (<c.150 sq m) | Clinc And Premises | 1 | | 44 | 1. <1,500 sq ft (<c.150 sq m) | Clinic And Premises | 1 | | 45 | 1. <1,500 sq ft (<c.150 sq m) | Clinic Room | 1 | | 46 | 1. <1,500 sq ft (<c.150 sq m) | Club And Premises | 1 | | 47 | 1. <1,500 sq ft (<c.150 sq m) | Club House And Premises | 1 | | 48 | 1. <1,500 sq ft (<c.150 sq m) | Coffee Bar And Premises | 1 | | 49 | 1. <1,500 sq ft (<c.150 sq m) | Cold Store And Premises | 1 | | 50 | 1. <1,500 sq ft (<c.150 sq m) | Communal Meeting Room | 1 | | 51 | 1. <1,500 sq ft (<c.150 sq m) | Community Centre And Premises | 1 | | 52 | 1. <1,500 sq ft (<c.150 sq m) | Container And Premises | 1 | | 53 | 1. <1,500 sq ft (<c.150 sq m) | Container Storage Site & Premises | 1 | | 54 | 1. <1,500 sq ft (<c.150 sq m) | Container Store And Premises | 1 | | 55 | 1. <1,500 sq ft (<c.150 sq m) | Container Stores And Premises | 1 | | 56 | 1. <1,500 sq ft (<c.150 sq m) | Containers Store And Premises | 1 | | 57 | 1. <1,500 sq ft (<c.150 sq m) | Containers Stores And Premises | 1 | | 58 | 1. <1,500 sq ft (<c.150 sq m) | Continuum Care School And Premises | 1 | | 59 | 1. <1,500 sq ft (<c.150 sq m) | Cookery School And Premises | 1 | | 60 | 1. <1,500 sq ft (<c.150 sq m) | Counselling Room And Premises | 1 | | 61 | 1. <1,500 sq ft (<c.150 sq m) | Cultural Centre And Premises | 1 | | 62 | 1. <1,500 sq ft (<c.150 sq m) | Cultural Education Centre And Premises | 1 | | 63 | 1. <1,500 sq ft (<c.150 sq m) | Dance Studio | 1 | | 64 | 1. <1,500 sq ft (<c.150 sq m) | Dance Studio And Premises | 1 | | 65 | 1. <1,500 sq ft (<c.150 sq m) | Demonstration Room And Premises | 1 | | 66 | 1. <1,500 sq ft (<c.150 sq m) | Dental Laboratory | 1 | | 67 | 1. <1,500 sq ft (<c.150 sq m) | Dental Laboratory And Premises | 1 | | 68 | 1. <1,500 sq ft (<c.150 sq m) | Denture Lab And Premises | 1 | | 69 | 1. <1,500 sq ft (<c.150 sq m) | Dog Grooming | 1 | | 70 | 1. <1,500 sq ft (<c.150 sq m) | Dog Grooming And Premises | 1 | | 71 | 1. <1,500 sq ft (<c.150 sq m) | Dog Grooming Parlour | 1 | | 72 | 1. <1,500 sq ft (<c.150 sq m) | Dog Grooming Parlour And Premises | 1 | | 73 | 1. <1,500 sq ft (<c.150 sq m) | Dog Grooming Pod | 1 | | 74 | 1. <1,500 sq ft (<c.150 sq m) | Dog Grooming Salon | 1 | | 75 | 1. <1,500 sq ft (<c.150 sq m) | Dog Parlour & Premises | 1 | | 76 | 1. <1,500 sq ft (<c.150 sq m) | Dog Parlour And Premises | 1 | | 77 | 1. <1,500 sq ft (<c.150 sq m) | Drop In Centre And Premises | 1 | | 78 | 1. <1,500 sq ft (<c.150 sq m) | Escape Room And Premises | 1 | | 79 | 1. <1,500 sq ft (<c.150 sq m) | Ex-Cold Store | 1 | | 80 | 1. <1,500 sq ft (<c.150 sq m) | Factory And Premises | 5 | | 81 | 1. <1,500 sq ft (<c.150 sq m) | Factory, Office And Premises | 1 | | 82 | 1. <1,500 sq ft (<c.150 sq m) | Family Centre And Premises | 1 | | 83 | 1. <1,500 sq ft (<c.150 sq m) | Farmshop And Premises (Partially Exempt) | 1 | | 84 | 1. <1,500 sq ft (<c.150 sq m) | Fitness And Beauty Centre And Premises | 1 | | 85 | 1. <1,500 sq ft (<c.150 sq m) | Fitness Centre And Premises | 1 | | 86 | 1. <1,500 sq ft (<c.150 sq m) | Fitness Room | 1 | | 87 | 1. <1,500 sq ft (<c.150 sq m) | Fitness Studio | 1 | | 88 | 1. <1,500 sq ft (<c.150 sq m) | Fitness Studio And Premises | 1 | | 89 | 1. <1,500 sq ft (<c.150 sq m) | Food Processing Unit And Shop | 1 | | 90 | 1. <1,500 sq ft (<c.150 sq m) | Food Takeaway And Premises | 1 | | 91 | 1. <1,500 sq ft (<c.150 sq m) | Forge, Store And Premises | 1 | | 92 | 1. <1,500 sq ft (<c.150 sq m) | Function Room And Premises | 1 | | 93 | 1. <1,500 sq ft (<c.150 sq m) | Funeral Directors & Premises | 1 | | 94 | 1. <1,500 sq ft (<c.150 sq m) | Gallery And Premises | 1 | | 95 | 1. <1,500 sq ft (<c.150 sq m) | Garage | 1 | | 96 | 1. <1,500 sq ft (<c.150 sq m) | Garage And Premises | 1 | | 97 | 1. <1,500 sq ft (<c.150 sq m) | Garages | 1 | | 98 | 1. <1,500 sq ft (<c.150 sq m) | Greenkeepers Store And Premises | 1 | | 99 | 1. <1,500 sq ft (<c.150 sq m) | Grooming Parlour | 1 | | 100 | 1. <1,500 sq ft (<c.150 sq m) | Grooming Parlour And Premises | 1 | | 101 | 1. <1,500 sq ft (<c.150 sq m) | Guest Suite And Premises | 1 | | 102 | 1. <1,500 sq ft (<c.150 sq m) | Gym And Premises | 1 | | 103 | 1. <1,500 sq ft (<c.150 sq m) | Gymnasium | 1 | | 104 | 1. <1,500 sq ft (<c.150 sq m) | Gymnasium And Premises | 1 | | 105 | 1. <1,500 sq ft (<c.150 sq m) | Hair Dressing Salon And Premises | 1 | | 106 | 1. <1,500 sq ft (<c.150 sq m) | Hair Salon And Premises | 1 | | 107 | 1. <1,500 sq ft (<c.150 sq m) | Hairdressing Salon | 1 | | 108 | 1. <1,500 sq ft (<c.150 sq m) | Hairdressing Salon And Premises | 1 | | 109 | 1. <1,500 sq ft (<c.150 sq m) | Hairdressing Unit And Premises | 1 | | 110 | 1. <1,500 sq ft (<c.150 sq m) | Hall And Premises | 1 | | 111 | 1. <1,500 sq ft (<c.150 sq m) | Hand Car Wash And Premises | 1 | | 112 | 1. <1,500 sq ft (<c.150 sq m) | Hangar | 1 | | 113 | 1. <1,500 sq ft (<c.150 sq m) | Hangar And Premises | 1 | | 114 | 1. <1,500 sq ft (<c.150 sq m) | Haulage Depot And Premises | 1 | | 115 | 1. <1,500 sq ft (<c.150 sq m) | Hollistic And Wellbeing Centre And Premises | 1 | | 116 | 1. <1,500 sq ft (<c.150 sq m) | Hot Food Takeaway And Premises | 1 | | 117 | 1. <1,500 sq ft (<c.150 sq m) | Hydratherapy Pool And Premises | 1 | | 118 | 1. <1,500 sq ft (<c.150 sq m) | Hydrotherapy Pool And Premises | 1 | | 119 | 1. <1,500 sq ft (<c.150 sq m) | Internet Cafe And Premises | 1 | | 120 | 1. <1,500 sq ft (<c.150 sq m) | Kiosk | 1 | | 121 | 1. <1,500 sq ft (<c.150 sq m) | Kiosk And Premises | 1 | | 122 | 1. <1,500 sq ft (<c.150 sq m) | Kitchen | 1 | | 123 | 1. <1,500 sq ft (<c.150 sq m) | Kitchen And Premises | 1 | | 124 | 1. <1,500 sq ft (<c.150 sq m) | Kitchen, Workshop And Premises | 1 | | 125 | 1. <1,500 sq ft (<c.150 sq m) | Kitchens And Premises | 1 | | 126 | 1. <1,500 sq ft (<c.150 sq m) | Laboratory And Premises | 1 | | 127 | 1. <1,500 sq ft (<c.150 sq m) | Land Used As Car Wash Area And Premises | 1 | | 128 | 1. <1,500 sq ft (<c.150 sq m) | Land Used For Car Display And Premises | 1 | | 129 | 1. <1,500 sq ft (<c.150 sq m) | Land Used For Parking | 1 | | 130 | 1. <1,500 sq ft (<c.150 sq m) | Land Used For Storage | 1 | | 131 | 1. <1,500 sq ft (<c.150 sq m) | Land Used For Storage And Premises | 6 | | 132 | 1. <1,500 sq ft (<c.150 sq m) | Land Used For Storage, Caravans And Premises | 1 | | 133 | 1. <1,500 sq ft (<c.150 sq m) | Land Used For Storage, Store And Premises | 1 | | 134 | 1. <1,500 sq ft (<c.150 sq m) | Lecture Room | 1 | | 135 | 1. <1,500 sq ft (<c.150 sq m) | Leisure Centre And Premises | 1 | | 136 | 1. <1,500 sq ft (<c.150 sq m) | Loading Bay | 1 | | 137 | 1. <1,500 sq ft (<c.150 sq m) | Lock Up Garage | 1 | | 138 | 1. <1,500 sq ft (<c.150 sq m) | Lorry Parking Area And Premises | 1 | | 139 | 1. <1,500 sq ft (<c.150 sq m) | Marketing Suite | 1 | | 140 | 1. <1,500 sq ft (<c.150 sq m) | Marketing Suite & Premises | 1 | | 141 | 1. <1,500 sq ft (<c.150 sq m) | Marketing Suite And Premises | 1 | | 142 | 1. <1,500 sq ft (<c.150 sq m) | Martial Arts Centre And Premises | 1 | | 143 | 1. <1,500 sq ft (<c.150 sq m) | Martial Arts Studio And Premises | 1 | | 144 | 1. <1,500 sq ft (<c.150 sq m) | Massage Spa And Premsies | 1 | | 145 | 1. <1,500 sq ft (<c.150 sq m) | Meeting Room And Premises | 1 | | 146 | 1. <1,500 sq ft (<c.150 sq m) | Meeting Rooms And Premises | 1 | | 147 | 1. <1,500 sq ft (<c.150 sq m) | Mortuary Chapel | 1 | | 148 | 1. <1,500 sq ft (<c.150 sq m) | Nail Bar And Premises | 1 | | 149 | 1. <1,500 sq ft (<c.150 sq m) | Nail Studio And Premises | 1 | | 150 | 1. <1,500 sq ft (<c.150 sq m) | Observatory And Premises | 1 | | 151 | 1. <1,500 sq ft (<c.150 sq m) | Office | 1 | | 152 | 1. <1,500 sq ft (<c.150 sq m) | Office & Premises | 1 | | 153 | 1. <1,500 sq ft (<c.150 sq m) | Office & Storage Premises | 1 | | 154 | 1. <1,500 sq ft (<c.150 sq m) | Office And Premises | 2 | | 155 | 1. <1,500 sq ft (<c.150 sq m) | Office And Storage Premises | 1 | | 156 | 1. <1,500 sq ft (<c.150 sq m) | Office And Store | 1 | | 157 | 1. <1,500 sq ft (<c.150 sq m) | Office In Industrial Unit And Premises | 1 | | 158 | 1. <1,500 sq ft (<c.150 sq m) | Office In Warehouse And Premises | 1 | | 159 | 1. <1,500 sq ft (<c.150 sq m) | Office Store And Premises | 1 | | 160 | 1. <1,500 sq ft (<c.150 sq m) | Office Stores And Premises | 1 | | 161 | 1. <1,500 sq ft (<c.150 sq m) | Office Used As Store | 1 | | 162 | 1. <1,500 sq ft (<c.150 sq m) | Office Workshop And Premises | 1 | | 163 | 1. <1,500 sq ft (<c.150 sq m) | Office, Photographic Studio And Premises | 1 | | 164 | 1. <1,500 sq ft (<c.150 sq m) | Office, Store And Premises | 1 | | 165 | 1. <1,500 sq ft (<c.150 sq m) | Office, Stores And Premises | 1 | | 166 | 1. <1,500 sq ft (<c.150 sq m) | Office, Workroom And Premises | 1 | | 167 | 1. <1,500 sq ft (<c.150 sq m) | Office, Workshop And Premises | 1 | | 168 | 1. <1,500 sq ft (<c.150 sq m) | Office/Studio And Premises | 1 | | 169 | 1. <1,500 sq ft (<c.150 sq m) | Office/Workroom And Premises | 1 | | 170 | 1. <1,500 sq ft (<c.150 sq m) | Offices | 1 | | 171 | 1. <1,500 sq ft (<c.150 sq m) | Offices & Premises | 1 | | 172 | 1. <1,500 sq ft (<c.150 sq m) | Offices And Premises | 84 | | 173 | 1. <1,500 sq ft (<c.150 sq m) | Offices And Premises (Part Exempt) | 1 | | 174 | 1. <1,500 sq ft (<c.150 sq m) | Offices Used As Stores | 1 | | 175 | 1. <1,500 sq ft (<c.150 sq m) | Offices Workroom And Premises | 1 | | 176 | 1. <1,500 sq ft (<c.150 sq m) | Offices, Car Space And Premises | 2 | | 177 | 1. <1,500 sq ft (<c.150 sq m) | Offices, Office And Premises | 1 | | 178 | 1. <1,500 sq ft (<c.150 sq m) | Offices, Store And Premises | 1 | | 179 | 1. <1,500 sq ft (<c.150 sq m) | Offioce And Premises | 1 | | 180 | 1. <1,500 sq ft (<c.150 sq m) | Open Fronted Store | 1 | | 181 | 1. <1,500 sq ft (<c.150 sq m) | Ortho/Dental Surgery & Premises | 1 | | 182 | 1. <1,500 sq ft (<c.150 sq m) | Parenting Centre And Premises | 1 | | 183 | 1. <1,500 sq ft (<c.150 sq m) | Party Room And Premises | 1 | | 184 | 1. <1,500 sq ft (<c.150 sq m) | Pet Grooming Parlour | 1 | | 185 | 1. <1,500 sq ft (<c.150 sq m) | Petrol Filling Station And Premises | 1 | | 186 | 1. <1,500 sq ft (<c.150 sq m) | Photographic Studio | 1 | | 187 | 1. <1,500 sq ft (<c.150 sq m) | Photographic Studio And Premises | 1 | | 188 | 1. <1,500 sq ft (<c.150 sq m) | Photography Studio | 1 | | 189 | 1. <1,500 sq ft (<c.150 sq m) | Photography Studio And Premises | 1 | | 190 | 1. <1,500 sq ft (<c.150 sq m) | Physio Room And Premises | 1 | | 191 | 1. <1,500 sq ft (<c.150 sq m) | Physiotherapy Clinic | 1 | | 192 | 1. <1,500 sq ft (<c.150 sq m) | Physiotherapy Clinic And Premises | 1 | | 193 | 1. <1,500 sq ft (<c.150 sq m) | Physiotherapy Studio And Premises | 1 | | 194 | 1. <1,500 sq ft (<c.150 sq m) | Physiothery Treatment Centre And Premises | 1 | | 195 | 1. <1,500 sq ft (<c.150 sq m) | Pilates Studio And Premises | 1 | | 196 | 1. <1,500 sq ft (<c.150 sq m) | Podiatry Clinic And Premises | 1 | | 197 | 1. <1,500 sq ft (<c.150 sq m) | Portable Building | 1 | | 198 | 1. <1,500 sq ft (<c.150 sq m) | Portable Buildings And Premises | 1 | | 199 | 1. <1,500 sq ft (<c.150 sq m) | Portacabin | 1 | | 200 | 1. <1,500 sq ft (<c.150 sq m) | Portacabin And Premises | 1 | | 201 | 1. <1,500 sq ft (<c.150 sq m) | Portacabins | 1 | | 202 | 1. <1,500 sq ft (<c.150 sq m) | Portakabin Office | 1 | | 203 | 1. <1,500 sq ft (<c.150 sq m) | Pottery Studio And Premises | 1 | | 204 | 1. <1,500 sq ft (<c.150 sq m) | Pysiotherapy Studio | 1 | | 205 | 1. <1,500 sq ft (<c.150 sq m) | Pysiotherapy Studio And Premises | 1 | | 206 | 1. <1,500 sq ft (<c.150 sq m) | Pysiotherapy Treatment Centre And Premises | 1 | | 207 | 1. <1,500 sq ft (<c.150 sq m) | Reception And Meeting Room | 1 | | 208 | 1. <1,500 sq ft (<c.150 sq m) | Recording Studio And Premises | 1 | | 209 | 1. <1,500 sq ft (<c.150 sq m) | Rest Room And Premises | 1 | | 210 | 1. <1,500 sq ft (<c.150 sq m) | Restaurant And Premises | 1 | | 211 | 1. <1,500 sq ft (<c.150 sq m) | Retail Unit | 1 | | 212 | 1. <1,500 sq ft (<c.150 sq m) | Retail Unit And Premises | 1 | | 213 | 1. <1,500 sq ft (<c.150 sq m) | Sales & Marketing Suite | 1 | | 214 | 1. <1,500 sq ft (<c.150 sq m) | Sales And Marketing Suite | 1 | | 215 | 1. <1,500 sq ft (<c.150 sq m) | Sales Area And Premises | 1 | | 216 | 1. <1,500 sq ft (<c.150 sq m) | Sales Office | 1 | | 217 | 1. <1,500 sq ft (<c.150 sq m) | Sales Office And Premises | 1 | | 218 | 1. <1,500 sq ft (<c.150 sq m) | Salon And Premises | 1 | | 219 | 1. <1,500 sq ft (<c.150 sq m) | Scrapyard And Premises | 1 | | 220 | 1. <1,500 sq ft (<c.150 sq m) | Security Office And Premises | 1 | | 221 | 1. <1,500 sq ft (<c.150 sq m) | Shop | 1 | | 222 | 1. <1,500 sq ft (<c.150 sq m) | Shop And Premises | 1 | | 223 | 1. <1,500 sq ft (<c.150 sq m) | Shop Unit | 1 | | 224 | 1. <1,500 sq ft (<c.150 sq m) | Shorwoom, Office And Premises | 1 | | 225 | 1. <1,500 sq ft (<c.150 sq m) | Showroom And Premises | 1 | | 226 | 1. <1,500 sq ft (<c.150 sq m) | Showroom, Office And Premises | 1 | | 227 | 1. <1,500 sq ft (<c.150 sq m) | Site Office & Premises | 1 | | 228 | 1. <1,500 sq ft (<c.150 sq m) | Site Office And Premises | 1 | | 229 | 1. <1,500 sq ft (<c.150 sq m) | Sorage Container | 1 | | 230 | 1. <1,500 sq ft (<c.150 sq m) | Sorting Office And Premise | 1 | | 231 | 1. <1,500 sq ft (<c.150 sq m) | Sorting Office And Premises | 1 | | 232 | 1. <1,500 sq ft (<c.150 sq m) | Sports Injury Clinic And Premises | 1 | | 233 | 1. <1,500 sq ft (<c.150 sq m) | Sports Therapy Clinic | 1 | | 234 | 1. <1,500 sq ft (<c.150 sq m) | Storage Container | 1 | | 235 | 1. <1,500 sq ft (<c.150 sq m) | Storage Container And Premises | 1 | | 236 | 1. <1,500 sq ft (<c.150 sq m) | Storage Container Site | 1 | | 237 | 1. <1,500 sq ft (<c.150 sq m) | Storage Container Site And Premises | 1 | | 238 | 1. <1,500 sq ft (<c.150 sq m) | Storage Containers | 1 | | 239 | 1. <1,500 sq ft (<c.150 sq m) | Storage Containers & Premises | 1 | | 240 | 1. <1,500 sq ft (<c.150 sq m) | Storage Containers And Premises | 1 | | 241 | 1. <1,500 sq ft (<c.150 sq m) | Storage Depot And Premises | 1 | | 242 | 1. <1,500 sq ft (<c.150 sq m) | Storage Land Store ( In Disrepair) And Premises | 1 | | 243 | 1. <1,500 sq ft (<c.150 sq m) | Storage Unit And Premises | 1 | | 244 | 1. <1,500 sq ft (<c.150 sq m) | Storage Yard And Premises | 1 | | 245 | 1. <1,500 sq ft (<c.150 sq m) | Store | 1 | | 246 | 1. <1,500 sq ft (<c.150 sq m) | Store & Premises | 1 | | 247 | 1. <1,500 sq ft (<c.150 sq m) | Store And Premise | 1 | | 248 | 1. <1,500 sq ft (<c.150 sq m) | Store And Premises | 31 | | 249 | 1. <1,500 sq ft (<c.150 sq m) | Store Office And Premises | 1 | | 250 | 1. <1,500 sq ft (<c.150 sq m) | Store Room And Premises | 1 | | 251 | 1. <1,500 sq ft (<c.150 sq m) | Store, Land And Premises | 1 | | 252 | 1. <1,500 sq ft (<c.150 sq m) | Store, Office And Premises | 1 | | 253 | 1. <1,500 sq ft (<c.150 sq m) | Store, Offices And Premises | 1 | | 254 | 1. <1,500 sq ft (<c.150 sq m) | Store, Store And Premises | 1 | | 255 | 1. <1,500 sq ft (<c.150 sq m) | Store, Workshop And Premises | 1 | | 256 | 1. <1,500 sq ft (<c.150 sq m) | Store,Office And Premises | 1 | | 257 | 1. <1,500 sq ft (<c.150 sq m) | Storeage Containers And Premises | 1 | | 258 | 1. <1,500 sq ft (<c.150 sq m) | Stores And Premises | 1 | | 259 | 1. <1,500 sq ft (<c.150 sq m) | Stores Sales And Premises | 1 | | 260 | 1. <1,500 sq ft (<c.150 sq m) | Strongroom And Premises | 1 | | 261 | 1. <1,500 sq ft (<c.150 sq m) | Studio | 1 | | 262 | 1. <1,500 sq ft (<c.150 sq m) | Studio & Premises | 1 | | 263 | 1. <1,500 sq ft (<c.150 sq m) | Studio And Premises | 1 | | 264 | 1. <1,500 sq ft (<c.150 sq m) | Studio Workshop | 1 | | 265 | 1. <1,500 sq ft (<c.150 sq m) | Stuidio And Premises | 1 | | 266 | 1. <1,500 sq ft (<c.150 sq m) | Surgery And Premises | 1 | | 267 | 1. <1,500 sq ft (<c.150 sq m) | Tanning Studio | 1 | | 268 | 1. <1,500 sq ft (<c.150 sq m) | Tanning Studio And Premises | 1 | | 269 | 1. <1,500 sq ft (<c.150 sq m) | Tattoo Parlour | 1 | | 270 | 1. <1,500 sq ft (<c.150 sq m) | Tattoo Parlour And Premises | 1 | | 271 | 1. <1,500 sq ft (<c.150 sq m) | Tattoo Studio | 1 | | 272 | 1. <1,500 sq ft (<c.150 sq m) | Tattoo Studio And Premises | 1 | | 273 | 1. <1,500 sq ft (<c.150 sq m) | Taxi Office And Premises | 1 | | 274 | 1. <1,500 sq ft (<c.150 sq m) | Therapy Centre And Premises | 1 | | 275 | 1. <1,500 sq ft (<c.150 sq m) | Therapy Clinic And Premises | 1 | | 276 | 1. <1,500 sq ft (<c.150 sq m) | Therapy Room And Premises | 1 | | 277 | 1. <1,500 sq ft (<c.150 sq m) | Therapy Rooms And Premises | 1 | | 278 | 1. <1,500 sq ft (<c.150 sq m) | Tourist Office&Premises | 1 | | 279 | 1. <1,500 sq ft (<c.150 sq m) | Trader Provided Area | 1 | | 280 | 1. <1,500 sq ft (<c.150 sq m) | Training Facility, Office And Premises | 1 | | 281 | 1. <1,500 sq ft (<c.150 sq m) | Training Room And Premises | 1 | | 282 | 1. <1,500 sq ft (<c.150 sq m) | Training Room, Kitchen And Premises | 1 | | 283 | 1. <1,500 sq ft (<c.150 sq m) | Training Rooms And Premises | 1 | | 284 | 1. <1,500 sq ft (<c.150 sq m) | Treatment Room | 1 | | 285 | 1. <1,500 sq ft (<c.150 sq m) | Treatment Room And Premises | 1 | | 286 | 1. <1,500 sq ft (<c.150 sq m) | Treatment Rooms | 1 | | 287 | 1. <1,500 sq ft (<c.150 sq m) | Vehicle Cleansing Depot And Premises | 1 | | 288 | 1. <1,500 sq ft (<c.150 sq m) | Vehicle Repair Training Room, Workshop And Premises | 1 | | 289 | 1. <1,500 sq ft (<c.150 sq m) | Vehicle Repair Workshop | 1 | | 290 | 1. <1,500 sq ft (<c.150 sq m) | Vehicle Repair Workshop And Premises | 5 | | 291 | 1. <1,500 sq ft (<c.150 sq m) | Vehicle Workshop And Premises | 1 | | 292 | 1. <1,500 sq ft (<c.150 sq m) | Venue, Practice & Meeting Rooms | 1 | | 293 | 1. <1,500 sq ft (<c.150 sq m) | Warehouse Office And Premises | 1 | | 294 | 1. <1,500 sq ft (<c.150 sq m) | Warehouse & Premises | 1 | | 295 | 1. <1,500 sq ft (<c.150 sq m) | Warehouse And Premises | 28 | | 296 | 1. <1,500 sq ft (<c.150 sq m) | Warehouse, Store And Premises | 1 | | 297 | 1. <1,500 sq ft (<c.150 sq m) | Warehouse, Workshop And Premises | 1 | | 298 | 1. <1,500 sq ft (<c.150 sq m) | Work Unit And Premises | 1 | | 299 | 1. <1,500 sq ft (<c.150 sq m) | Workroom And Premises | 1 | | 300 | 1. <1,500 sq ft (<c.150 sq m) | Works Office, Store And Premises | 1 | | 301 | 1. <1,500 sq ft (<c.150 sq m) | Works Offices & Premises | 1 | | 302 | 1. <1,500 sq ft (<c.150 sq m) | Workshop | 1 | | 303 | 1. <1,500 sq ft (<c.150 sq m) | Workshop & Premises | 1 | | 304 | 1. <1,500 sq ft (<c.150 sq m) | Workshop & Premises | 1 | | 305 | 1. <1,500 sq ft (<c.150 sq m) | Workshop And Premises | 72 | | 306 | 1. <1,500 sq ft (<c.150 sq m) | Workshop And Prems | 1 | | 307 | 1. <1,500 sq ft (<c.150 sq m) | Workshop Land And Premises | 1 | | 308 | 1. <1,500 sq ft (<c.150 sq m) | Workshop Office And Premises | 1 | | 309 | 1. <1,500 sq ft (<c.150 sq m) | Workshop Shop And Premises | 1 | | 310 | 1. <1,500 sq ft (<c.150 sq m) | Workshop Store And Premises | 1 | | 311 | 1. <1,500 sq ft (<c.150 sq m) | Workshop Stuidio | 1 | | 312 | 1. <1,500 sq ft (<c.150 sq m) | Workshop, Car Sales And Premises | 1 | | 313 | 1. <1,500 sq ft (<c.150 sq m) | Workshop, Land And Premises | 1 | | 314 | 1. <1,500 sq ft (<c.150 sq m) | Workshop, Office And Premises | 1 | | 315 | 1. <1,500 sq ft (<c.150 sq m) | Workshop, Retail, Store And Premises | 1 | | 316 | 1. <1,500 sq ft (<c.150 sq m) | Workshop, Store And Premises | 1 | | 317 | 1. <1,500 sq ft (<c.150 sq m) | Workshop, Workshop And Premises | 1 | | 318 | 1. <1,500 sq ft (<c.150 sq m) | Workshop,Portacabins And Premises | 1 | | 319 | 1. <1,500 sq ft (<c.150 sq m) | Workshops And Premises | 1 | | 320 | 1. <1,500 sq ft (<c.150 sq m) | Workspace And Premises | 1 | | 321 | 1. <1,500 sq ft (<c.150 sq m) | Worm Distribution Store And Premises | 1 | | 322 | 1. <1,500 sq ft (<c.150 sq m) | Yard Used For Storage And Premises | 1 | | 323 | 1. <1,500 sq ft (<c.150 sq m) | Yard, Office And Premises | 1 | | 324 | 1. <1,500 sq ft (<c.150 sq m) | Yoga Centre And Premises | 1 | | 325 | 1. <1,500 sq ft (<c.150 sq m) | Yoga Studio And Premises | 1 | | 326 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Academy, Offices And Premises | 1 | | 327 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Amateur Boxing Club And Premises | 1 | | 328 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Auction House And Premises | 1 | | 329 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Bakery And Premises | 1 | | 330 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Brewery And Premises | 1 | | 331 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Building Under Reconconstruction | 1 | | 332 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Building Under Reconstruction | 1 | | 333 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Building Under Reconstuction | 1 | | 334 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Buildings In Disrepair, Land, Car Parking And Premises | 1 | | 335 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Business Unit And Premises | 1 | | 336 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Butchery And Premises | 1 | | 337 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Cafe And Premises | 1 | | 338 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Canine Creche And Premises | 1 | | 339 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Canteen And Premises | 1 | | 340 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Car Sales Warehouse & Premises | 1 | | 341 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Car Showroom And Premises | 1 | | 342 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Car Store, Office And Premises | 1 | | 343 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Car Store,Office & Premises | 1 | | 344 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Chapel Of Rest, Garages And Premises | 1 | | 345 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Childrens Play Centre And Premises | 1 | | 346 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Club And Premises | 1 | | 347 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Club House And Premises | 1 | | 348 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Community Cafe, Offices And Premises | 1 | | 349 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Community Centre And Premises | 1 | | 350 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Concrete Testing Labroratory & Prems | 1 | | 351 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Concrete Testing, Laboratory And Premises | 1 | | 352 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Contractors Accommodation | 1 | | 353 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Contractors Compound And Premises | 1 | | 354 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Counselling Centre | 1 | | 355 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Craft Distillery And Premises | 1 | | 356 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Dance Drama School And Premises | 1 | | 357 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Dance Studio And Premises | 1 | | 358 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Dance Studios | 1 | | 359 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Day Care Centre And Premises | 1 | | 360 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Dental Clinic And Premises | 1 | | 361 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Dental Workshop And Premises | 1 | | 362 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Dog Day Care Centre And Premises | 1 | | 363 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Dog Training School & Premises | 1 | | 364 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Dog Training School And Premises | 1 | | 365 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Education Centre & Premises | 1 | | 366 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Factory & Premises | 1 | | 367 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Factory And Premises | 6 | | 368 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Factory Office And Premises | 1 | | 369 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Factory Offices And Premises | 1 | | 370 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Factory, Office And Premises | 1 | | 371 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Fitness Centre & Premises | 1 | | 372 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Fitness Centre And Premises | 1 | | 373 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Function Rooms | 1 | | 374 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Garage And Premises | 1 | | 375 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Goalkeeping Academy And Premises | 1 | | 376 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Gym | 1 | | 377 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Gym / Fitness Centre | 1 | | 378 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Gym And Premises | 1 | | 379 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Gymnasium & Premises | 1 | | 380 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Gymnasium And Premises | 1 | | 381 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Hall And Premises | 1 | | 382 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Hand Car Wash And Premises | 1 | | 383 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Hand Car Wash Site And Premises | 1 | | 384 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Hangar | 1 | | 385 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Hangar And Premises | 1 | | 386 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Hangar,Warehouse And Premises | 1 | | 387 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Hanger And Premises | 1 | | 388 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Healthcare Centre And Premises | 1 | | 389 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Healthcare Offices And Premises | 1 | | 390 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Hi-Tech Industrial Premises | 1 | | 391 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Indoor Climbing Centre And Premises | 1 | | 392 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Kitchen & Premises | 1 | | 393 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Kitchen Furniture Workshop And Premises | 1 | | 394 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Land Used As Hand Carwash And Premises | 1 | | 395 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Land Used For Car Parking | 1 | | 396 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Land Used For Storage | 1 | | 397 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Land Used For Storage & Premises | 1 | | 398 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Land Used For Storage And Premises | 21 | | 399 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Land Used For Storage, Office And Premises | 1 | | 400 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Land Used For Storage, Workshop And Premises | 1 | | 401 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Mot Centre And Premises | 1 | | 402 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Museum And Premises | 1 | | 403 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Nursery And Premises | 1 | | 404 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Office And Premises | 1 | | 405 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Office, Rooms & Premises | 1 | | 406 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Office, Showroom And Premises | 1 | | 407 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices Premises | 1 | | 408 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices & Premises | 2 | | 409 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices An Premises (Beyond Economic Repair) | 1 | | 410 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices And Premises | 42 | | 411 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices And Premises (Beyond Economic Repair) | 1 | | 412 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices And Premises (Part Exempt) | 1 | | 413 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices In Warehouse And Premises | 1 | | 414 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices Jetty And Premises | 1 | | 415 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices Store And Premises | 1 | | 416 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices Workshop And Premises | 1 | | 417 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices, Jetty And Premises | 1 | | 418 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices, Land And Premises | 1 | | 419 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices, Office And Premises | 1 | | 420 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices, Storage Land And Premises | 1 | | 421 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices, Store And Premises | 1 | | 422 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices, Stores And Premises | 1 | | 423 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices, Warehouse And Premises | 1 | | 424 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices, Workshop And Premises | 1 | | 425 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Offices,Workshop And Premises | 1 | | 426 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Parenting Centre And Premises | 1 | | 427 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Pet Groomer And Premises | 1 | | 428 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Pet Groomers And Premises | 1 | | 429 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Petrol Filling Station And Premises | 1 | | 430 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Play Centre And Premises | 1 | | 431 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Playcentre And Premises | 1 | | 432 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Property Under Reconstruction | 1 | | 433 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Respite Care Home And Premises | 1 | | 434 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Retail Warehouse And Premises | 1 | | 435 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Retail Workshop And Premises | 1 | | 436 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Retail, Workshop And Premises | 1 | | 437 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Sales Office And Premises | 1 | | 438 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Sea Cadet Unit And Premises | 1 | | 439 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Shop And Premises | 1 | | 440 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Shop Workshop And Premises | 1 | | 441 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Showroom Office And Premises | 1 | | 442 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Showroom Warehouse And Premises | 1 | | 443 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Showroom, Offices And Premises | 1 | | 444 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Showroom, Warehouse And Premises | 1 | | 445 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Showroom, Workshop And Premises | 1 | | 446 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Small Arena And Premises | 1 | | 447 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Sorting Office And Premises | 3 | | 448 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Storage And Premises | 1 | | 449 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Storage Depot & Premises | 1 | | 450 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Storage Depot And Premises | 1 | | 451 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Storage Depot, Office And Premises | 1 | | 452 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Storage Unit And Premises | 1 | | 453 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Storage Yard And Premises | 1 | | 454 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Store | 1 | | 455 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Store (In Disrepair) | 2 | | 456 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Store And Premises | 16 | | 457 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Store And Premises (In Disrepair) | 1 | | 458 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Store Office And Premises | 1 | | 459 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Store, Car Park And Premises | 1 | | 460 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Store, Land And Premises | 1 | | 461 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Store, Office And Premises | 1 | | 462 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Store, Offices And Premises | 1 | | 463 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Stores And Premises | 1 | | 464 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Stores Office And Premises | 1 | | 465 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Stores Yard And Premises | 1 | | 466 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Stores, Offices And Premises | 1 | | 467 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Stores,Office And Premises | 1 | | 468 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Stores/Land For Storage And Premises | 1 | | 469 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Studio And Premises | 1 | | 470 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Studio In Warehouse And Premises | 1 | | 471 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Surgery And Premises | 1 | | 472 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Tattoo Parlour And Premises | 1 | | 473 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Training Centre And Premises | 1 | | 474 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Training Rooms & Premises (Partially Exempt) | 1 | | 475 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Training Salon And Premises | 1 | | 476 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Trampoline Centre And Premises | 1 | | 477 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Tuition Centre And Premises | 1 | | 478 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Tyre And Exhaust Centre And Premises | 1 | | 479 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Used Car Sales, Workshop And Premises | 1 | | 480 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Van Hire Site And Premises | 1 | | 481 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Vehicle Repair Workshop And Premises | 5 | | 482 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Vehicle Repair Workshop Showroom And Premises | 1 | | 483 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Vehicle Repair Workshop, Car Sales And Premises | 1 | | 484 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Vehicle Repair Workshop, Car Showroom And Premises | 1 | | 485 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Vehicle Repair Workshop,Office And Premises | 1 | | 486 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Vehicle Sales And Premises | 1 | | 487 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Vehicle Storage Land | 1 | | 488 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse & Premises | 1 | | 489 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse And Premises | 41 | | 490 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse Office And Premises | 1 | | 491 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse Offices And Premises | 1 | | 492 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse Sales And Premises | 1 | | 493 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse Showroom And Premises | 1 | | 494 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse, Cold Store And Premises | 1 | | 495 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse, Office And Premises | 1 | | 496 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse, Offices & Premises | 1 | | 497 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse, Offices And Premises | 1 | | 498 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse, Sales Area And Premises | 1 | | 499 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse, Shop And Premises | 1 | | 500 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse, Showroom And Premises | 1 | | 501 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse, Stables And Premises | 1 | | 502 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse, Store And Premises | 2 | | 503 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse, Workshop And Premises | 1 | | 504 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse,Car Sales And Premises | 1 | | 505 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Warehouse,Office And Premises | 1 | | 506 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Water Bottling Centre And Premises | 1 | | 507 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Works And Premises | 1 | | 508 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop & Premises | 1 | | 509 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop And Cookery School | 1 | | 510 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop And Premises | 57 | | 511 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop And Sleeping Quarters | 1 | | 512 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop Car Sales And Premises | 1 | | 513 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop Car Sales Site And Premises | 1 | | 514 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop Offices And Premises | 1 | | 515 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop Showroom And Premises | 1 | | 516 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop Showrrom And Premises | 1 | | 517 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop Storage Land And Premises | 1 | | 518 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop Stores And Premises | 1 | | 519 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop Studio And Premises | 1 | | 520 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop Yard And Premises | 3 | | 521 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop, Car Sales And Premises | 1 | | 522 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop, Gallery And Premises | 1 | | 523 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop, Land & Premises | 1 | | 524 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop, Land And Premises | 1 | | 525 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop, Office & Premises | 1 | | 526 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop, Office And Premises | 3 | | 527 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop, Offices And Premises | 2 | | 528 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop, Shop And Premises | 1 | | 529 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop, Shop, Store And Premises | 1 | | 530 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop, Showroom And Premises | 1 | | 531 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop, Storage Land & Premises | 1 | | 532 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop, Store And Premises | 1 | | 533 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop, Workshop And Premises | 1 | | 534 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop, Yard And Premises | 1 | | 535 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop,Offices And Premises | 1 | | 536 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshop,Storage Land And Premises | 1 | | 537 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshops And Premises | 1 | | 538 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshops Offices And Premises | 2 | | 539 | 2. 1,500 to 5,499 sq ft (c.150 to 499 sq m) | Workshops, Land And Premises | 1 | | 540 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Acting School | 1 | | 541 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Aircraft Hangar & Premises | 1 | | 542 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Bakery And Premises | 1 | | 543 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Builders Merchant And Premises | 1 | | 544 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Builders Merchants And Premises | 1 | | 545 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Builders Merchants Yard & Premises | 1 | | 546 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Builders Yard & Premises | 1 | | 547 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Builders Yard And Premises | 1 | | 548 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Building Under Reconstruction | 1 | | 549 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Business Unit And Premise | 1 | | 550 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Business Unit And Premises | 1 | | 551 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Car Park And Premises | 1 | | 552 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Car Sales And Premises | 1 | | 553 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Car Sales Centre And Premises | 1 | | 554 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Childrens Adventure Centre And Premises | 1 | | 555 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Childrens Centre And Premises | 1 | | 556 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Childrens Play Barn And Premises | 1 | | 557 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Childrens Play Centre And Premises | 1 | | 558 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Club And Premises | 1 | | 559 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Community Centre And Premises | 1 | | 560 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Contractors Accomodation | 1 | | 561 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Dinghy Park | 1 | | 562 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Dog Day Care And Premises | 1 | | 563 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | End Of Life Vehicle Facility And Premises | 1 | | 564 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Factory And Premises | 12 | | 565 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Factory, Workshop And Premises | 1 | | 566 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Fitness Centre And Premises | 1 | | 567 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Garage And Premises | 1 | | 568 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Gymnasium | 1 | | 569 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Gymnasium And Premises | 1 | | 570 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Hangar And Premises | 1 | | 571 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Job Centre And Premises | 1 | | 572 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Knackers Yard,Workshop And Premises | 1 | | 573 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Lab, Factory & Premises | 1 | | 574 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Laboratories And Premises | 1 | | 575 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Land Used For Car Parking | 1 | | 576 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Land Used For Storage | 1 | | 577 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Land Used For Storage And Premises | 7 | | 578 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Land Used For Storage, Caravan Pitches And Premises | 1 | | 579 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Land Used For Storage, Workshop And Premises | 1 | | 580 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Land Used For Vehicle Sales And Premises | 1 | | 581 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Land, Offices & Premises | 1 | | 582 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Offices & Premises | 1 | | 583 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Offices And Premises | 12 | | 584 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Offices And Premises ( Part Exempt ) | 1 | | 585 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Offices Laboratories And Premises | 1 | | 586 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Offices, Warehouse And Premises | 1 | | 587 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Offices, Workshop And Premises | 1 | | 588 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Offices, Workshops And Premises | 1 | | 589 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Offices,Warehouses & Premises | 1 | | 590 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Property Undergoing Reconstruction | 1 | | 591 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Reatail Warehouse & Premises | 1 | | 592 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Retail And Premises | 1 | | 593 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Retail Warehouse & Premises | 1 | | 594 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Royal Mail Sorting Office | 1 | | 595 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Showroom, Workshop And Premises | 1 | | 596 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Sorting Centre And Premises | 1 | | 597 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Sorting Office | 1 | | 598 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Sorting Office & Premises | 1 | | 599 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Sorting Office And Premises | 1 | | 600 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Sorting Offices And Premises | 1 | | 601 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Storage And Premises | 1 | | 602 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Storage Depot And Premises | 3 | | 603 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Storage Unit And Premises | 1 | | 604 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Store & Premises | 1 | | 605 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Store And Premises | 2 | | 606 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Store, Office And Premises | 1 | | 607 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Stores And Premises | 1 | | 608 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Stores And Premises | 1 | | 609 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Therapy Centre & Premises | 1 | | 610 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Training Centre And Premises | 1 | | 611 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Vehicle Repair Workshop And Premises | 2 | | 612 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Warehouse & Premises | 1 | | 613 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Warehouse And Premises | 7 | | 614 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Warehouse Office And Premises | 1 | | 615 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Warehouse Offices And Premises | 1 | | 616 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Warehouse Showroom And Premises | 1 | | 617 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Warehouse, Land And Premises | 1 | | 618 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Warehouse, Office And Premises | 1 | | 619 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Warehouse, Offices And Premises | 1 | | 620 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Warehouse, Workshop And Premises | 1 | | 621 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop & Premises | 1 | | 622 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop And Premises | 12 | | 623 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop And Premises (Part Exempt) | 1 | | 624 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop Offices And Premises | 1 | | 625 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop Showrooms And Premises | 1 | | 626 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop Storage Land And Premises | 1 | | 627 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop, Office & Premises | 1 | | 628 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop, Office And Premises | 1 | | 629 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop, Offices & Premises | 1 | | 630 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop, Offices And Premises | 1 | | 631 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop, Storage Containers And Premises | 1 | | 632 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop, Stores And Premises | 1 | | 633 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop, Workshop And Premises | 1 | | 634 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop, Yard And Premises | 1 | | 635 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop,Office And Premises | 1 | | 636 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshop/Land And Premises | 1 | | 637 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshops And Premises | 1 | | 638 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshops Offices And Premises | 1 | | 639 | 3. 5,500 to 10,999 sq ft (c.500 to 999 sq m) | Workshops, Stores And Premises | 1 | | 640 | 4. 11,000+ sq ft (c.1,000+ sq m) | Agri. Digger Driving/Training Land | 1 | | 641 | 4. 11,000+ sq ft (c.1,000+ sq m) | Aircraft Hangar & Premises | 1 | | 642 | 4. 11,000+ sq ft (c.1,000+ sq m) | Aircraft Hangar And Premises | 1 | | 643 | 4. 11,000+ sq ft (c.1,000+ sq m) | Animal Exercise Land | 1 | | 644 | 4. 11,000+ sq ft (c.1,000+ sq m) | Builders Compound And Premises | 1 | | 645 | 4. 11,000+ sq ft (c.1,000+ sq m) | Builders Merchant And Premises | 2 | | 646 | 4. 11,000+ sq ft (c.1,000+ sq m) | Builders Merchants And Premises | 1 | | 647 | 4. 11,000+ sq ft (c.1,000+ sq m) | Builders Yard And Premises | 1 | | 648 | 4. 11,000+ sq ft (c.1,000+ sq m) | Building Works | 1 | | 649 | 4. 11,000+ sq ft (c.1,000+ sq m) | Business Centre And Premises | 1 | | 650 | 4. 11,000+ sq ft (c.1,000+ sq m) | Business Unit And Premises | 1 | | 651 | 4. 11,000+ sq ft (c.1,000+ sq m) | Car Hall Offices And Premises | 1 | | 652 | 4. 11,000+ sq ft (c.1,000+ sq m) | Car Park, Office And Premises | 1 | | 653 | 4. 11,000+ sq ft (c.1,000+ sq m) | Carhall, Offices And Premises | 1 | | 654 | 4. 11,000+ sq ft (c.1,000+ sq m) | Commercial Yard And Premises | 1 | | 655 | 4. 11,000+ sq ft (c.1,000+ sq m) | Computer Centre And Premises | 1 | | 656 | 4. 11,000+ sq ft (c.1,000+ sq m) | Delivery/Sorting Office | 1 | | 657 | 4. 11,000+ sq ft (c.1,000+ sq m) | Depot And Premises | 1 | | 658 | 4. 11,000+ sq ft (c.1,000+ sq m) | Distribution Centre & Premises | 1 | | 659 | 4. 11,000+ sq ft (c.1,000+ sq m) | End Of Life Vehicle Facility And Premises | 1 | | 660 | 4. 11,000+ sq ft (c.1,000+ sq m) | Factories And Premises | 1 | | 661 | 4. 11,000+ sq ft (c.1,000+ sq m) | Factory & Premises | 1 | | 662 | 4. 11,000+ sq ft (c.1,000+ sq m) | Factory And Premises | 25 | | 663 | 4. 11,000+ sq ft (c.1,000+ sq m) | Factory, Office And Premises | 2 | | 664 | 4. 11,000+ sq ft (c.1,000+ sq m) | Factory, Offices And Premises | 1 | | 665 | 4. 11,000+ sq ft (c.1,000+ sq m) | Factory, Stores And Premises | 1 | | 666 | 4. 11,000+ sq ft (c.1,000+ sq m) | Flying School And Premises | 1 | | 667 | 4. 11,000+ sq ft (c.1,000+ sq m) | Garden Centre And Premises | 1 | | 668 | 4. 11,000+ sq ft (c.1,000+ sq m) | Hangar | 1 | | 669 | 4. 11,000+ sq ft (c.1,000+ sq m) | Hangar & Premises | 1 | | 670 | 4. 11,000+ sq ft (c.1,000+ sq m) | Hangar And Premises | 1 | | 671 | 4. 11,000+ sq ft (c.1,000+ sq m) | Hangar Workshops And Premises | 1 | | 672 | 4. 11,000+ sq ft (c.1,000+ sq m) | Hangar Workshops Offices And Premises | 2 | | 673 | 4. 11,000+ sq ft (c.1,000+ sq m) | Hangar, Aircraft Stands And Premises | 1 | | 674 | 4. 11,000+ sq ft (c.1,000+ sq m) | Hangar, Offices And Premises | 1 | | 675 | 4. 11,000+ sq ft (c.1,000+ sq m) | Hangars, Aircraft Stand And Premises | 1 | | 676 | 4. 11,000+ sq ft (c.1,000+ sq m) | Hangars, Workshops And Premises | 1 | | 677 | 4. 11,000+ sq ft (c.1,000+ sq m) | Hanger And Premises | 1 | | 678 | 4. 11,000+ sq ft (c.1,000+ sq m) | Haulage Yard And Premises | 1 | | 679 | 4. 11,000+ sq ft (c.1,000+ sq m) | Indoor Trampline Park | 1 | | 680 | 4. 11,000+ sq ft (c.1,000+ sq m) | Indoor Trampolining Park | 1 | | 681 | 4. 11,000+ sq ft (c.1,000+ sq m) | L.P.G Station And Premises | 1 | | 682 | 4. 11,000+ sq ft (c.1,000+ sq m) | Laboratories And Premises | 1 | | 683 | 4. 11,000+ sq ft (c.1,000+ sq m) | Land Used For Bus Storage And Premises | 1 | | 684 | 4. 11,000+ sq ft (c.1,000+ sq m) | Land Used For Storage | 1 | | 685 | 4. 11,000+ sq ft (c.1,000+ sq m) | Land Used For Storage & Premises | 1 | | 686 | 4. 11,000+ sq ft (c.1,000+ sq m) | Land Used For Storage And Premises | 30 | | 687 | 4. 11,000+ sq ft (c.1,000+ sq m) | Land Used For Storage, Warehouse And Premises | 1 | | 688 | 4. 11,000+ sq ft (c.1,000+ sq m) | Land Used For Storage, Workshop And Premises | 1 | | 689 | 4. 11,000+ sq ft (c.1,000+ sq m) | Land Used For Van Storage And Premises | 1 | | 690 | 4. 11,000+ sq ft (c.1,000+ sq m) | Land Workshop And Premises | 1 | | 691 | 4. 11,000+ sq ft (c.1,000+ sq m) | Lorry Aprk And Premises | 1 | | 692 | 4. 11,000+ sq ft (c.1,000+ sq m) | Lorry Compound | 1 | | 693 | 4. 11,000+ sq ft (c.1,000+ sq m) | Lorry Park & Premises | 1 | | 694 | 4. 11,000+ sq ft (c.1,000+ sq m) | Lorry Park And Premises | 1 | | 695 | 4. 11,000+ sq ft (c.1,000+ sq m) | Mandir And Premise (Part Exempt) | 1 | | 696 | 4. 11,000+ sq ft (c.1,000+ sq m) | Newspaper Printing Works And Premises | 1 | | 697 | 4. 11,000+ sq ft (c.1,000+ sq m) | Office Warehouse And Premises | 1 | | 698 | 4. 11,000+ sq ft (c.1,000+ sq m) | Offices & Premises | 2 | | 699 | 4. 11,000+ sq ft (c.1,000+ sq m) | Offices And Premises | 6 | | 700 | 4. 11,000+ sq ft (c.1,000+ sq m) | Offices And Premises (Part Exempt) | 1 | | 701 | 4. 11,000+ sq ft (c.1,000+ sq m) | Offices Warehouse And Premises | 1 | | 702 | 4. 11,000+ sq ft (c.1,000+ sq m) | Offices, Factory And Premises | 1 | | 703 | 4. 11,000+ sq ft (c.1,000+ sq m) | Offices, Laboratories And Premises | 1 | | 704 | 4. 11,000+ sq ft (c.1,000+ sq m) | Offices, Land And Premises | 1 | | 705 | 4. 11,000+ sq ft (c.1,000+ sq m) | Offices, Office And Premises | 1 | | 706 | 4. 11,000+ sq ft (c.1,000+ sq m) | Offices, Warehouse And Premises | 1 | | 707 | 4. 11,000+ sq ft (c.1,000+ sq m) | Offices, Workshop And Premises | 1 | | 708 | 4. 11,000+ sq ft (c.1,000+ sq m) | Offices, Workshops And Premises | 1 | | 709 | 4. 11,000+ sq ft (c.1,000+ sq m) | Pet Cemetery | 1 | | 710 | 4. 11,000+ sq ft (c.1,000+ sq m) | Post Office Sorting Office | 1 | | 711 | 4. 11,000+ sq ft (c.1,000+ sq m) | Post Office Sorting Office & Premises | 1 | | 712 | 4. 11,000+ sq ft (c.1,000+ sq m) | Printing Works And Premises | 1 | | 713 | 4. 11,000+ sq ft (c.1,000+ sq m) | Property Under Reconstruction | 1 | | 714 | 4. 11,000+ sq ft (c.1,000+ sq m) | Property Undergoing Reconstruction | 1 | | 715 | 4. 11,000+ sq ft (c.1,000+ sq m) | Rail Freight Terminal And Premises | 1 | | 716 | 4. 11,000+ sq ft (c.1,000+ sq m) | Recycling Centre And Premises | 1 | | 717 | 4. 11,000+ sq ft (c.1,000+ sq m) | Research Centre & Premises | 1 | | 718 | 4. 11,000+ sq ft (c.1,000+ sq m) | Research Centre And Premises | 1 | | 719 | 4. 11,000+ sq ft (c.1,000+ sq m) | Salvage/Breakers Yard And Premises | 1 | | 720 | 4. 11,000+ sq ft (c.1,000+ sq m) | Self Storage And Premises | 1 | | 721 | 4. 11,000+ sq ft (c.1,000+ sq m) | Site Of Former Lorry Park | 1 | | 722 | 4. 11,000+ sq ft (c.1,000+ sq m) | Sorting Centre And Premises | 1 | | 723 | 4. 11,000+ sq ft (c.1,000+ sq m) | Sorting Office | 1 | | 724 | 4. 11,000+ sq ft (c.1,000+ sq m) | Storage Depot And Premises | 2 | | 725 | 4. 11,000+ sq ft (c.1,000+ sq m) | Storage Depot Jetty And Premises | 1 | | 726 | 4. 11,000+ sq ft (c.1,000+ sq m) | Storage Land Workshop And Premises | 1 | | 727 | 4. 11,000+ sq ft (c.1,000+ sq m) | Store And Premises | 1 | | 728 | 4. 11,000+ sq ft (c.1,000+ sq m) | Store, Land And Premises | 1 | | 729 | 4. 11,000+ sq ft (c.1,000+ sq m) | Stores Showroom And Premises | 1 | | 730 | 4. 11,000+ sq ft (c.1,000+ sq m) | Studio Workshop And Premises | 1 | | 731 | 4. 11,000+ sq ft (c.1,000+ sq m) | Switch Centre | 1 | | 732 | 4. 11,000+ sq ft (c.1,000+ sq m) | Timber Yard, Stores, Showroom & Premises | 1 | | 733 | 4. 11,000+ sq ft (c.1,000+ sq m) | Timber Yard,Stores,Showroom & Premises | 1 | | 734 | 4. 11,000+ sq ft (c.1,000+ sq m) | Training Centre And Premises | 1 | | 735 | 4. 11,000+ sq ft (c.1,000+ sq m) | Trampoline Park And Premises | 1 | | 736 | 4. 11,000+ sq ft (c.1,000+ sq m) | Vehicle Repair Workshop And Premises | 1 | | 737 | 4. 11,000+ sq ft (c.1,000+ sq m) | Warehouse & Premises | 1 | | 738 | 4. 11,000+ sq ft (c.1,000+ sq m) | Warehouse , Office And Premises | 1 | | 739 | 4. 11,000+ sq ft (c.1,000+ sq m) | Warehouse And Premises | 20 | | 740 | 4. 11,000+ sq ft (c.1,000+ sq m) | Warehouse Office And Premises | 1 | | 741 | 4. 11,000+ sq ft (c.1,000+ sq m) | Warehouse Offices And Premises | 1 | | 742 | 4. 11,000+ sq ft (c.1,000+ sq m) | Warehouse Showroom And Premises | 1 | | 743 | 4. 11,000+ sq ft (c.1,000+ sq m) | Warehouse Used For Retail And Premises | 1 | | 744 | 4. 11,000+ sq ft (c.1,000+ sq m) | Warehouse, Office And Premises | 1 | | 745 | 4. 11,000+ sq ft (c.1,000+ sq m) | Warehouse, Offices And Premises | 1 | | 746 | 4. 11,000+ sq ft (c.1,000+ sq m) | Warehouse, Store And Premises | 1 | | 747 | 4. 11,000+ sq ft (c.1,000+ sq m) | Warehouse, Workshop And Premises | 1 | | 748 | 4. 11,000+ sq ft (c.1,000+ sq m) | Warehouse,Workshop & Offices And Premises | 1 | | 749 | 4. 11,000+ sq ft (c.1,000+ sq m) | Warehouses And Premises | 1 | | 750 | 4. 11,000+ sq ft (c.1,000+ sq m) | Warehouses, Offices And Premises | 1 | | 751 | 4. 11,000+ sq ft (c.1,000+ sq m) | Waste Transfer Site And Premises | 1 | | 752 | 4. 11,000+ sq ft (c.1,000+ sq m) | Works And Premises | 1 | | 753 | 4. 11,000+ sq ft (c.1,000+ sq m) | Workshop And Premises | 7 | | 754 | 4. 11,000+ sq ft (c.1,000+ sq m) | Workshop Office And Premises | 1 | | 755 | 4. 11,000+ sq ft (c.1,000+ sq m) | Workshop Offices And Premises | 1 | | 756 | 4. 11,000+ sq ft (c.1,000+ sq m) | Workshop Warehouse And Premises | 1 | | 757 | 4. 11,000+ sq ft (c.1,000+ sq m) | Workshop, Offices And Premises | 2 | | 758 | 4. 11,000+ sq ft (c.1,000+ sq m) | Workshop, Showroom And Premises | 1 | | 759 | 4. 11,000+ sq ft (c.1,000+ sq m) | Workshop, Warehouse And Premises | 1 | | 760 | 4. 11,000+ sq ft (c.1,000+ sq m) | Workshop,Offce And Premises | 1 | | 761 | 4. 11,000+ sq ft (c.1,000+ sq m) | Workshop,Offices And Premises | 1 | | 762 | 4. 11,000+ sq ft (c.1,000+ sq m) | Workshops And Premises | 1 | | 763 | 4. 11,000+ sq ft (c.1,000+ sq m) | Workshops And Premises | 1 | | 764 | 4. 11,000+ sq ft (c.1,000+ sq m) | Workshops And Premises | 2 | | 765 | 4. 11,000+ sq ft (c.1,000+ sq m) | Workshops, Offices, Equestrian Centre And Premises | 1 | +-----+----------------------------------------------+--------------------------------------------------------+--------------+
#Create unique Local Authority and County mappings
unique_Local_authorities_and_Counties = Processed_Data_VOA[['Local_Authority', 'County']].dropna().drop_duplicates()
#Tabulate local authorities to their respective counties
Local_authority_per_County = unique_Local_authorities_and_Counties.groupby('Local_Authority')['County'].apply(lambda x: ','.join(x)).reset_index()
#Print results
print(Local_authority_per_County)
Local_Authority County 0 Basildon ESSEX 1 Braintree ESSEX,SUFFOLK 2 Brentwood ESSEX 3 Castle Point ESSEX 4 Chelmsford ESSEX 5 Colchester ESSEX,SUFFOLK 6 Epping Forest ESSEX,HERTS 7 Harlow ESSEX 8 Maldon ESSEX 9 Rochford ESSEX 10 Southend-On-Sea ESSEX 11 Tendring ESSEX 12 Thurrock ESSEX 13 Uttlesford ESSEX,HERTS
INTERACTIVE PLOTS FOR FIRM COUNTS IN ACROSS DIFFERENT CATEGORIES IN PROCESSED_DATA_VOA:
#DEFINE THE CATEGORIZATION FUNCTIONS FOR THE INTERACTIVE PLOTS
#Categorize by geography
def Cat_by_geography(Processed_Data_VOA):
# Group by county
County_group = Processed_Data_VOA.groupby('County')['FirmName'].nunique().reset_index(name='Firm_count')
# Group by local authority
Local_Authority_group = Processed_Data_VOA.groupby('Local_Authority')['FirmName'].nunique().reset_index(name='Firm_count')
return County_group, Local_Authority_group
# Categorize by business type
def Cat_by_business_type(Processed_Data_VOA):
# Group by business type
Business_Type_group = Processed_Data_VOA.groupby('PriDescText')['FirmName'].nunique().reset_index(name='Firm_count')
return Business_Type_group
#Categorize by business size of area occupied
def Cat_by_area_size(Processed_Data_VOA):
# Group by area combined
Business_AreaSize_group = Processed_Data_VOA.groupby(['AreaCombined_Group', 'PriDescText', 'Local_Authority'])['FirmName'].nunique().reset_index(name='Firm_count')
return Business_AreaSize_group
#Group categories
County_group, Local_Authority_group = Cat_by_geography(Processed_Data_VOA)
Business_Type_group = Cat_by_business_type(Processed_Data_VOA)
Business_AreaSize_group = Cat_by_area_size(Processed_Data_VOA)
#create dataframes for each group
County_group_df = pd.DataFrame(County_group)
Local_Authority_group_df = pd.DataFrame(Local_Authority_group)
Business_Type_group_df = pd.DataFrame(Business_Type_group)
Business_AreaSize_group_df = pd.DataFrame(Business_AreaSize_group)
#inspect columns to verify presence
print("County_group_df columns:", County_group_df.columns)
print("Local_Authority_group_df columns:", Local_Authority_group_df.columns)
County_group_df columns: Index(['County', 'Firm_count'], dtype='object') Local_Authority_group_df columns: Index(['Local_Authority', 'Firm_count'], dtype='object')
#Merge the County information with the Business_AreaSize_group
merged_data1 = pd.merge(Business_AreaSize_group, unique_Local_authorities_and_Counties, on='Local_Authority', how='left')
print(tabulate(merged_data1.head(100), headers='keys', tablefmt='psql'))
+----+-------------------------------+--------------------------------------+-------------------+--------------+----------+ | | AreaCombined_Group | PriDescText | Local_Authority | Firm_count | County | |----+-------------------------------+--------------------------------------+-------------------+--------------+----------| | 0 | 1. <1,500 sq ft (<c.150 sq m) | 0ffices And Premises | Epping Forest | 1 | ESSEX | | 1 | 1. <1,500 sq ft (<c.150 sq m) | 0ffices And Premises | Epping Forest | 1 | HERTS | | 2 | 1. <1,500 sq ft (<c.150 sq m) | Airfield Support Centre | Chelmsford | 1 | ESSEX | | 3 | 1. <1,500 sq ft (<c.150 sq m) | Archive Museum And Premises | Epping Forest | 1 | ESSEX | | 4 | 1. <1,500 sq ft (<c.150 sq m) | Archive Museum And Premises | Epping Forest | 1 | HERTS | | 5 | 1. <1,500 sq ft (<c.150 sq m) | Art Gallery & Premises | Colchester | 1 | ESSEX | | 6 | 1. <1,500 sq ft (<c.150 sq m) | Art Gallery & Premises | Colchester | 1 | SUFFOLK | | 7 | 1. <1,500 sq ft (<c.150 sq m) | Art Gallery And Premises | Tendring | 1 | ESSEX | | 8 | 1. <1,500 sq ft (<c.150 sq m) | Art Studio & Premises | Colchester | 1 | ESSEX | | 9 | 1. <1,500 sq ft (<c.150 sq m) | Art Studio & Premises | Colchester | 1 | SUFFOLK | | 10 | 1. <1,500 sq ft (<c.150 sq m) | Art Studio And Classroom Premises | Rochford | 1 | ESSEX | | 11 | 1. <1,500 sq ft (<c.150 sq m) | Art Studio And Premises | Colchester | 1 | ESSEX | | 12 | 1. <1,500 sq ft (<c.150 sq m) | Art Studio And Premises | Colchester | 1 | SUFFOLK | | 13 | 1. <1,500 sq ft (<c.150 sq m) | Artists Studio | Harlow | 1 | ESSEX | | 14 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Clinc | Maldon | 1 | ESSEX | | 15 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Pod And Premises | Southend-On-Sea | 1 | ESSEX | | 16 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Room & Premises | Tendring | 1 | ESSEX | | 17 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Room And Premises | Brentwood | 1 | ESSEX | | 18 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Room And Premises | Castle Point | 1 | ESSEX | | 19 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Room And Premises | Chelmsford | 1 | ESSEX | | 20 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Room And Premises | Colchester | 1 | ESSEX | | 21 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Room And Premises | Colchester | 1 | SUFFOLK | | 22 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon | Chelmsford | 1 | ESSEX | | 23 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon | Colchester | 1 | ESSEX | | 24 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon | Colchester | 1 | SUFFOLK | | 25 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon & Premises | Tendring | 1 | ESSEX | | 26 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon & Premises | Thurrock | 1 | ESSEX | | 27 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon & Premises | Uttlesford | 1 | ESSEX | | 28 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon & Premises | Uttlesford | 1 | HERTS | | 29 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon And Premises | Braintree | 1 | ESSEX | | 30 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon And Premises | Braintree | 1 | SUFFOLK | | 31 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon And Premises | Chelmsford | 1 | ESSEX | | 32 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon And Premises | Colchester | 1 | ESSEX | | 33 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon And Premises | Colchester | 1 | SUFFOLK | | 34 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon And Premises | Epping Forest | 1 | ESSEX | | 35 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon And Premises | Epping Forest | 1 | HERTS | | 36 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon And Premises | Harlow | 1 | ESSEX | | 37 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon And Premises | Maldon | 1 | ESSEX | | 38 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon And Premises | Rochford | 1 | ESSEX | | 39 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon And Premises | Southend-On-Sea | 1 | ESSEX | | 40 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon, Office & Premises | Braintree | 1 | ESSEX | | 41 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Salon, Office & Premises | Braintree | 1 | SUFFOLK | | 42 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Treatment Room | Basildon | 1 | ESSEX | | 43 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Treatment Room | Brentwood | 1 | ESSEX | | 44 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Treatment Room | Thurrock | 1 | ESSEX | | 45 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Treatment Room And Premises | Basildon | 1 | ESSEX | | 46 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Treatment Room And Premises | Southend-On-Sea | 1 | ESSEX | | 47 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Treatment Rooms And Premises | Chelmsford | 1 | ESSEX | | 48 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Treatments Room | Epping Forest | 1 | ESSEX | | 49 | 1. <1,500 sq ft (<c.150 sq m) | Beauty Treatments Room | Epping Forest | 1 | HERTS | | 50 | 1. <1,500 sq ft (<c.150 sq m) | Bonded Store | Rochford | 1 | ESSEX | | 51 | 1. <1,500 sq ft (<c.150 sq m) | Boxing Gym And Premises | Tendring | 1 | ESSEX | | 52 | 1. <1,500 sq ft (<c.150 sq m) | Builders Yard | Brentwood | 1 | ESSEX | | 53 | 1. <1,500 sq ft (<c.150 sq m) | Building Under Reconstruction | Chelmsford | 1 | ESSEX | | 54 | 1. <1,500 sq ft (<c.150 sq m) | Building Under Reconstruction | Harlow | 1 | ESSEX | | 55 | 1. <1,500 sq ft (<c.150 sq m) | Building Undergoing Reconstruction | Harlow | 1 | ESSEX | | 56 | 1. <1,500 sq ft (<c.150 sq m) | Building Undergoing Works | Epping Forest | 1 | ESSEX | | 57 | 1. <1,500 sq ft (<c.150 sq m) | Building Undergoing Works | Epping Forest | 1 | HERTS | | 58 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | Basildon | 1 | ESSEX | | 59 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | Braintree | 1 | ESSEX | | 60 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | Braintree | 1 | SUFFOLK | | 61 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | Chelmsford | 1 | ESSEX | | 62 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | Colchester | 1 | ESSEX | | 63 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | Colchester | 1 | SUFFOLK | | 64 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | Epping Forest | 1 | ESSEX | | 65 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | Epping Forest | 1 | HERTS | | 66 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | Harlow | 1 | ESSEX | | 67 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | Maldon | 1 | ESSEX | | 68 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | Rochford | 1 | ESSEX | | 69 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | Southend-On-Sea | 1 | ESSEX | | 70 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | Tendring | 1 | ESSEX | | 71 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit And Premises | Thurrock | 1 | ESSEX | | 72 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit, Store And Premises | Thurrock | 1 | ESSEX | | 73 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit, Workshop And Premises | Epping Forest | 1 | ESSEX | | 74 | 1. <1,500 sq ft (<c.150 sq m) | Business Unit, Workshop And Premises | Epping Forest | 1 | HERTS | | 75 | 1. <1,500 sq ft (<c.150 sq m) | Cafe | Chelmsford | 1 | ESSEX | | 76 | 1. <1,500 sq ft (<c.150 sq m) | Cafe And Premises | Basildon | 1 | ESSEX | | 77 | 1. <1,500 sq ft (<c.150 sq m) | Cafe And Premises | Braintree | 1 | ESSEX | | 78 | 1. <1,500 sq ft (<c.150 sq m) | Cafe And Premises | Braintree | 1 | SUFFOLK | | 79 | 1. <1,500 sq ft (<c.150 sq m) | Cafe And Premises | Chelmsford | 1 | ESSEX | | 80 | 1. <1,500 sq ft (<c.150 sq m) | Cafe And Premises | Tendring | 1 | ESSEX | | 81 | 1. <1,500 sq ft (<c.150 sq m) | Cafe And Premises | Thurrock | 1 | ESSEX | | 82 | 1. <1,500 sq ft (<c.150 sq m) | Canine Fitness Centre And Premises | Tendring | 1 | ESSEX | | 83 | 1. <1,500 sq ft (<c.150 sq m) | Canopy And Premises | Epping Forest | 1 | ESSEX | | 84 | 1. <1,500 sq ft (<c.150 sq m) | Canopy And Premises | Epping Forest | 1 | HERTS | | 85 | 1. <1,500 sq ft (<c.150 sq m) | Car Hire Office And Premises | Chelmsford | 1 | ESSEX | | 86 | 1. <1,500 sq ft (<c.150 sq m) | Car Wash And Premises | Chelmsford | 1 | ESSEX | | 87 | 1. <1,500 sq ft (<c.150 sq m) | Caravan And Premises | Braintree | 1 | ESSEX | | 88 | 1. <1,500 sq ft (<c.150 sq m) | Caravan And Premises | Braintree | 1 | SUFFOLK | | 89 | 1. <1,500 sq ft (<c.150 sq m) | Catering Cabin | Brentwood | 1 | ESSEX | | 90 | 1. <1,500 sq ft (<c.150 sq m) | Catering Kitchen And Premises | Chelmsford | 1 | ESSEX | | 91 | 1. <1,500 sq ft (<c.150 sq m) | Catering Kitchen And Premises | Rochford | 1 | ESSEX | | 92 | 1. <1,500 sq ft (<c.150 sq m) | Catering Unit And Premises | Maldon | 1 | ESSEX | | 93 | 1. <1,500 sq ft (<c.150 sq m) | Chiropody Clinic And Premises | Chelmsford | 1 | ESSEX | | 94 | 1. <1,500 sq ft (<c.150 sq m) | Chiropratic Clinic And Premises | Rochford | 1 | ESSEX | | 95 | 1. <1,500 sq ft (<c.150 sq m) | Classroom And Premises | Braintree | 1 | ESSEX | | 96 | 1. <1,500 sq ft (<c.150 sq m) | Classroom And Premises | Braintree | 1 | SUFFOLK | | 97 | 1. <1,500 sq ft (<c.150 sq m) | Classroom And Premises | Southend-On-Sea | 1 | ESSEX | | 98 | 1. <1,500 sq ft (<c.150 sq m) | Classroom And Premises | Tendring | 1 | ESSEX | | 99 | 1. <1,500 sq ft (<c.150 sq m) | Clinc And Premises | Colchester | 1 | ESSEX | +----+-------------------------------+--------------------------------------+-------------------+--------------+----------+
#Create interactive scatter plot using Plotly
Business_AreaSize_group_Scatterplot = px.scatter(
merged_data1,
x='Firm_count',
y='AreaCombined_Group',
title='SCATTER PLOT DISTRIBUTION OF BUSINESSES BY SIZES OF AREAS OCCUPIED IN PROCESSED_DATA_VOA',
labels={'Firm_count': 'Number of Businesses', 'AreaCombined_Group': 'Size Category'},
template='plotly_white',
color='PriDescText',
hover_data={'AreaCombined_Group': False, 'Firm_count': ':.0f', 'PriDescText': True, 'County': True, 'Local_Authority': True}
)
#Update layout for the plot
Business_AreaSize_group_Scatterplot.update_layout(
xaxis_title='Number of Businesses',
yaxis_title='Area Size Category',
showlegend=False,
xaxis={'categoryorder': 'total descending'},
legend_title_text='Business Type',
legend=dict(
orientation='h',
yanchor='bottom',
y=1.02,
xanchor='right',
x=1
)
)
#display the scatter plot
Business_AreaSize_group_Scatterplot.show()
#Save the plot in the working folder
#Use write_image for saving plotly figures
Business_AreaSize_group_Scatterplot.write_image('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\SCATTER_PLOT_DISTRIBUTION_OF_BUSINESS_ACCOMODATIONS_BY_SIZES_OF_AREAS_OCCUPIED.png')
KEY INSIGHTS AND ANALYSIS FOR THE SCATTER PLOT DISTRIBUTION OF BUSINESSES BY SIZES OF AREAS OCCUPIED IN THE PROCESSED_DATA_VOA DATASET
1.DISTRIBUTION ACROSS AREA SIZE CATEGORIES
Analysis: The interactive scatter plot visualizes businesses based on the sizes of the areas they occupy, divided into four distinct area size categories:
Category 1: Less than 1,500 sq ft (less than approximately 150 sq m)
Category 2: 1,500 to 5,499 sq ft (approximately 150 to 499 sq m)
Category 3: 5,500 to 10,999 sq ft (approximately 500 to 999 sq m)
Category 4: 11,000+ sq ft (more than approximately 1,000 sq m)
The scatter plot displays that businesses are fairly evenly distributed across these categories, with no significant clustering in any specific size range in the dataset.
Key Insight: This even distribution suggests that businesses vary greatly in terms of the space they occupy. Some businesses require large accommodation spaces, potentially indicating industrial or large retail operations, while others operate within much smaller spaces, possibly indicating smaller retail outlets or service-based businesses.
2.CONCENTRATION OF BUSINESSES
Analysis: The interactive scatter plot indicates that for each category, there are multiple instances of businesses occupying similar-sized areas, with the most frequent occurrence seen in the smallest area size category of business accommodations.
Key Insight: The concentration of businesses in the smaller size categories could imply a higher prevalence of small to medium-sized enterprises (SMEs), which typically require less accommodation space. This trend could be indicative of a robust SME sector within the region, which could be a key driver of local economic activity.
3.LARGE AREA BUSINESSES(AREA SIZE CATEGORY 4)
Analysis: The interactive scatter plot shows a few businesses occupying accommodation areas greater than 11,000 sq ft (more than 1,000 sq m), but these are fewer in comparison to the smaller area size categories of business accommodations occupied in the dataset.
Key Insight: The smaller number of businesses occupying large areas in the dataset might suggest that such spaces are reserved for specific types of operations, like manufacturing plants, large-scale warehouses, or big-box retail stores. The lower frequency of such businesses could reflect either a lower demand for large spaces or a scarcity of such properties.
4.IMPLICATIONS FOR PROPERTY DEVELOPERS AND PLANNERS
Analysis: The data on area sizes of accommodations occupied by businesses is crucial for developers and urban planners as it highlights the types of spaces that are in demand.
Key Insight: With a significant number of businesses occupying smaller spaces as displayed by the interactive scatter plot, developers might focus on creating more flexible, smaller commercial units. Conversely, the presence of businesses in larger business accommodation spaces, though less frequent, indicates a niche market that could benefit from more targeted development efforts to accommodate larger operations.
5.ECONOMIC IMPLICATIONS
Analysis: The diverse range of business sizes occupying varying amounts of accommodations suggests a diverse economy, with businesses ranging from small-scale operations to larger enterprises.
Key Insight: The varied distribution across different accommodation sizes indicates that the local economy is not overly dependent on a single type of business. This diversification can be seen as a positive sign, potentially leading to more resilience in the face of economic fluctuations.
DATA APPLICATIONS: For business owners, understanding the distribution of business accommodation sizes in the dataset can help new businesses gauge what type of space might be most suitable and available in the area.
For investors, the analysis highlights potential opportunities in both small and large commercial properties, depending on the investor's strategy and target market.
For urban planners, insights into the space requirements of local businesses can guide zoning decisions and the development of commercial areas, ensuring a good mix of property sizes to meet varying business needs in terms of business accommodations to occupy.
This analysis provides a comprehensive overview of the distribution of business accommodations by area size in the Processed_Data_VOA dataset, offering key insights that are valuable for stakeholders across multiple sectors, including real estate, investment, and urban planning.
#Interactive scatter Plot for firm counts of businesses by Counties in Processed_Data_VOA
county_scatter_plot = px.scatter(
County_group_df,
x='County',
y='Firm_count',
size='Firm_count',
color='County',
title='SCATTER PLOT OF BUSINESS DISTRIBUTION OF FIRM COUNTS BY COUNTY IN PROCESSED_DATA_VOA'
)
#display interactive plot
county_scatter_plot.show()
#Save the plot in the working folder
#Use write_image for saving plotly figures
county_scatter_plot.write_image('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\SCATTER_PLOT_OF_BUSINESS_DISTRIBUTION_OF_FIRM_COUNTS_BY_COUNTY.png')
KEY INSIGHTS AND ANALYSIS ON THE INTERACTIVE SCATTER PLOT FOR BUSINESS DISTRIBUTION OF FIRM COUNTS BY COUNTY IN THE PROCESSED_DATA_VOA DATASET
1.OVERVIEW OF BUSINESS DISTRIBUTION
Analysis: The interactive scatter plot displays the distribution of firm counts across three counties: Essex, Hertfordshire, and Suffolk. The size of the bubble represents the number of firms in each county, and the position of the bubble along the Y-axis shows the firm count.
Essex: displays a significantly higher firm count compared to the other counties, with over 500 firms represented.
Herts: displays a very minimal firm count, with only a small dot barely visible on the plot with 14 firm counts.
Suffolk: Similar to Herts, Suffolk also shows a very minimal number of firms with 2 firm counts only.
Key Insight: Essex is the dominant county in terms of firm counts as displayed by the plot. This suggests that Essex might be a central hub for businesses in the region, potentially offering better infrastructure, resources, or economic incentives that attract more firms. Conversely, Herts and Suffolk have a very small presence, indicating that these counties might be less commercially developed or less attractive to businesses.
2.IMPLICATIONS FOR ECONOMIC DEVELOPMENT
Analysis: The disparity in firm counts as displayed by the scatter plot, between Essex and the other two counties may indicate differences in economic policies, infrastructure, or market conditions. Essex's ability to attract and sustain a large number of firms could be due to several factors such as a more developed transportation network, availability of skilled labor, proximity to major markets, incentives for business development.
Key Insight: Policymakers and economic developers in Herts and Suffolk may need to explore strategies to attract more businesses which could include improving infrastructure, offering business incentives, or creating business-friendly environments to stimulate economic growth.
3.CONSIDERATIONS FOR INVESTORS AND BUSINESS OWNERS
Analysis: For investors and business owners, Essex represents a more competitive environment for businesses to occupy with a higher concentration of firm counts which could pose as both an advantage, due to the presence of an established business ecosystem, and a challenge, due to potentially higher competition.
Key Insight: Essex is likely to offer more networking opportunities, a robust supply chain, and access to a larger customer base, making it an attractive location for new and expanding businesses. However, Herts and Suffolk might offer lower competition and opportunities for growth in emerging markets.
4.STRATEGIC LOCATION PLANNING
Analysis: The concentration of firms in Essex suggests that businesses seeking to occupy business accomodations in a thriving economic zone might favor this county. In contrast, Herts and Suffolk could be targeted by businesses looking for untapped markets or regions where they can establish a dominant presence.
Key Insight: For new entrants or expanding companies, Essex provides an environment with established market dynamics, while Herts and Suffolk might appeal to those looking to pioneer new markets or take advantage of potential growth areas.
DATA APPLICATIONS: For policymakers, this data can help identify the need for targeted economic development in Herts and Suffolk, possibly through incentives or improved business infrastructure.
For businesses, companies can use this information to strategize the location of their business accomodations based on the level of competition and market potential in each county.
For investors, understanding where the majority of firms are concentrated can help in making informed decisions about where to allocate resources for maximum returns.
This analysis provides a clear view of the business distribution across counties using an interactive scatter plot to highlight Essex's dominance and suggesting potential opportunities for growth and development in Herts and Suffolk.
#Interactive bar chart for firm counts of businesses by Local Authorities in Processed_Data_VOA
local_authority_plot = px.bar(
Local_Authority_group_df,
x='Local_Authority',
y='Firm_count',
color='Local_Authority',
title='BUSINESS DISTRIBUTION OF FIRM COUNTS BY LOCAL AUTHORITIES IN PROCESSED_DATA_VOA'
)
local_authority_plot.show()
#Save the plot in the working folder
#Use write_image for saving plotly figures
local_authority_plot.write_image('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\BUSINESS_DISTRIBUTION_OF_FIRM_COUNTS_BY_LOCAL_AUTHORITIES.png')
KEY INSIGHTS AND ANALYSIS ON THE INTERACTIVE PLOT OF BUSINESS DISTRIBUTION OF FIRM COUNTS BY LOCAL AUTHORITIES IN THE PROCESSED_DATA_VOA DATASET
1.OVERVIEW OF BUSINESS DISTRIBUTION BY LOCAL AUTHORITY
Analysis: The interactive bar chart shows the distribution of firm counts across various local authorities in the dataset. The height of the bars represents the number of firms in each local authority.
Epping Forest: Stands out with the highest number of firms, exceeding 119.
Harlow: Follows with a significant firm count, close to 87.
Thurrock and Chelmsford: Both show moderate firm counts of 57 and 43, with Thurrock slightly higher than Chelmsford.
Other Local Authorities: Basildon, Colchester, Uttlesford, and Tendring have lower firm counts in the dataset, generally ranging from 40 to 60.
Key Insight: Epping Forest and Harlow are the dominant local authorities in terms of business presence. These areas likely provide favorable conditions for business operations, such as infrastructure, accessibility, and a supportive business environment. The other local authorities, though relevant, have a smaller share of the business population.
2.IMPLICATIONS FOR LOCAL ECONOMIC DEVELOPMENT
Analysis: The disparity in firm counts between local authorities as displayed by the plot may reflect differences in local economic policies, infrastructure development, and business support services.
Epping Forest and Harlow: Their strong performance in the dataset could be attributed to better connectivity, proximity to major markets, or active economic development programs.
Thurrock and Chelmsford: While having fewer firms than Epping Forest and Harlow, they still hold a significant number of businesses in the dataset, indicating stable economic activity.
Less Represented Authorities displayed: These areas may benefit from targeted interventions to attract more businesses, such as offering incentives or improving local amenities.
Key Insight: Policymakers in less represented local authorities in the dataset may need to consider strategies to boost business activity. This could include investing in infrastructure, offering tax breaks, or improving business services to make these areas more attractive to entrepreneurs.
3.STRATEGIC INSIGHTS FOR BUSINESSES AND INVESTORS
Analysis: For businesses looking to establish or expand their presence, Epping Forest and Harlow appear to be the most vibrant areas to seek business accommodation. However, this also means higher competition. Other authorities like Thurrock and Chelmsford might offer a balanced environment with moderate competition and good growth potential.
Key Insight: Epping Forest and Harlow should be considered prime locations for businesses that thrive in competitive, bustling environments. While authorities with fewer firms might be ideal for businesses looking to tap into less saturated markets.
4.RECOMMENDATIONS FOR LOCAL AUTHORITIES
Analysis: Local authorities with lower firm counts could consider implementing policies to stimulate business growth and expansion. This could involve:
Business development programs: Offering training and support for entrepreneurs.
Infrastructure improvements: Enhancing transport, digital connectivity, and utilities to make the area more appealing.
Marketing and promotion: Highlighting the benefits of operating in these localities to attract new businesses.
Key Insight: Local authorities like Brentwood, Castle Point, and Maldon, which have relatively low firm counts, should focus on creating a more business-friendly environment to attract and retain firms, potentially narrowing the gap with the leading local authorities.
DATA APPLICATIONS:
For Policymakers, this data provides a clear indication of which local authorities are thriving and which might require more support to grow their business communities for a wider outreach.
For businesses, companies can use this information to choose locations for their business accommodations based on the level of competition and available market opportunities.
For investors, understanding the distribution of firms can guide investment decisions, particularly in areas with growing business communities.
This analysis provides a comprehensive understanding of the distribution of firms across local authorities using an interactive scatter plot to highlight areas of strength and potential opportunities for growth and development.
#Interactive Treemap for Firm counts of Business types in Processed_Data_VOA
business_type_treemap = px.treemap(
Business_Type_group_df,
path=[px.Constant('All'), 'PriDescText'],
values='Firm_count',
title='TREEMAP DISTRIBUTION OF FIRM COUNT OF BUSINESS TYPES IN PROCESSED_DATA_VOA'
)
business_type_treemap.update_traces(root_color="lightgrey")
business_type_treemap.show()
#Save the plot in the working folder
#Use write_image for saving plotly figures
business_type_treemap.write_image('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\TREEMAP_DISTRIBUTION_OF_FIRM_COUNT_OF_BUSINESS_TYPES.png')
KEY INSIGHTS AND ANALYSIS ON THE TREE MAP DISTRIBUTION OF FIRM COUNT BY BUSINESS TYPES IN THE PROCESSED_DATA_VOA DATASET
1.OVERVIEW OF BUSINESS TYPE DISTRIBUTION
Analysis: The interactive treemap provides a visual representation of the distribution of firm counts across different business types in the dataset with the size of each rectangle corresponding to the number of firms within specified category.
Workshop and Premises: This category dominates the treemap, indicating it has the highest number of firms with the size of the rectangle suggesting a significant concentration of businesses in this category.
Offices and Premises: This category also occupies a substantial area of the treemap, indicating a high number of firms focused on office-based operations in the dataset.
Warehouse and Premises: Another large section in the plot, highlighting the importance of warehousing in the business landscape.
Store and Premises: Slightly smaller than the above categories, it still represents a considerable number of businesses, reflecting the presence of retail and storage operations.
Key Insight: The business environment is heavily weighted towards business accommodations such as Workshop and Premises, Offices and Premises, Warehouse and Premises. These categories likely represent the backbone of the local economy, providing essential services and goods. The prominence of these categories suggests that the region has a well-developed infrastructure to support manufacturing, office-based activities, and storage/logistics.
2.DIVERSITY OF BUSINESS TYPES
Analysis: Beyond the major categories, the interactive treemap shows a vast array of smaller rectangles representing various other business types of accommodation. The sheer number of different colors and sizes indicates a highly diversified business landscape, with many niche markets and specialized businesses.
Key Insight: The diversity of business types suggests a robust and resilient economy capable of supporting a wide range of industries which could be beneficial in mitigating economic shocks, as the local economy is not overly reliant on a single industry or business type.
3.IMPLICATIONS FOR BUSINESS STRATEGY
Analysis: Businesses considering entry into this market should note the saturation in the Workshop and Premises and the Offices and Premises categories. While these areas are thriving, new entrants might face stiff competition. Conversely, the smaller rectangles represent potential opportunities in less saturated markets where the competition might be lower.
Key Insight: Entrepreneurs and businesses might consider targeting less saturated business categories, where the market is not as crowded, to find unique opportunities and niches. Established firms in the dominant categories might focus on innovation and differentiation to maintain their competitive edge.
4.STRATEGIC RECOMMENDATIONS
New businesses should consider exploring opportunities in the less dominant business categories where there may be less competition and potential for growth.
For established businesses, companies in dominant categories like Workshop and Premises and Offices and Premises should focus on improving efficiency, customer service, and innovative offerings to stay competitive in a crowded market.
For policymakers, the concentration in a few business types might warrant support for diversification to ensure long-term economic stability and reduce dependency on a few key industries.
DATA APPLICATIONS:
Business Development: Understanding the distribution of business types as displayed by the interactive treemap helps in identifying potential market gaps and opportunities for expansion.
Economic Planning: Policymakers can use this information of this plot to promote balanced economic growth and support underrepresented business types.
Investment: Investors can identify which business sectors are thriving and which might offer new investment opportunities.
This analysis of the interactive treemap highlights the significant concentration in a few key business categories while also emphasizing the overall diversity within the local economy. This balanced approach between dominant and niche markets presents various opportunities and challenges for businesses, investors, and policymakers.
KEY INSIGHTS AND ANALYSIS: SUNBURST DISTRIBUTION FOR AREA SIZE OF ACCOMMODATIONS OCCUPIED BY BUSINESSES IN THE PROCESSED_DATA_VOA DATASET
1.OVERVIEW OF AREA SIZE DISTRIBUTION
Analysis: The interactive sunburst chart visualizes the distribution of businesses in the dataset based on the area size of accommodations they occupy. The chart is segmented into different area size categories:
<1,500 sq ft (≈<150 sq m) 1,500 to 5,499 sq ft (≈150 to 499 sq m) 5,500 to 10,999 sq ft (≈500 to 999 sq m) 11,000+ sq ft (≈1,000+ sq m)
The visual distribution indicates that a majority of businesses occupy smaller accommodation spaces, particularly in the <1,500 sq ft category, while fewer businesses occupy larger accommodation spaces.
Key Insight: Most businesses are housed in relatively small accommodations, suggesting that the local business environment might be dominated by small to medium-sized enterprises (SMEs) that do not require large physical spaces. This could imply a focus on service-based industries, retail operations, or small-scale manufacturing.
2.DOMINANCE OF SMALLER AREA CATEGORIES
Analysis: The largest section of the interactive sunburst chart is dedicated to the smallest area size category (<1,500 sq ft) which suggests that there is a high density of businesses that operate within a limited physical footprint in the dataset.
Key Insight: The dominance of smaller accommodations indicates a potential demand for affordable and compact commercial spaces suggesting that new businesses or startups may prefer these smaller spaces to minimize overhead costs.
3.IMPLICATIONS FOR COMMERCIAL REAL ESTATE
Analysis: The distribution implies that there is a greater demand for smaller business accommodations and in contrast, larger business accommodations (11,000+ sq ft) are less in demand, which could influence rental prices and vacancy rates in these categories.
Key Insight: Real estate developers and property managers might consider focusing on creating or optimizing smaller commercial spaces to meet this demand. For larger properties, there may be a need to offer flexible leasing options or consider converting these spaces into smaller units to attract more tenants.
4.STRATEGIC RECOMMENDATIONS
For real estate developers, the trend towards smaller business accommodation spaces suggests an opportunity to develop or modify properties into smaller units that can be leased at a lower cost, which may attract a larger number of small businesses.
For businesses, companies looking to establish a presence in this area should consider the availability and competitive pricing of smaller business accommodations, which seem to be the preferred option for many local businesses.
For local authorities, understanding the accommodation requirements of businesses can help in urban planning and zoning decisions, ensuring that there is an adequate supply of the right types of commercial spaces to support economic growth.
DATA APPLICATIONS:
Urban planning: This analysis can help city planners ensure that zoning regulations are aligned with the demand for small commercial spaces for business accommodations.
Commercial leasing: Leasing agents can use this information to target potential tenants more effectively, offering business accommodations that align with market demand.
Business strategy: Entrepreneurs can use this insight to make informed decisions about location and space requirements for business accomodations when entering the market.
The interactive sunburst chart provides a clear indication of the predominant business accommodation sizes within the area of the dataset. It underscores the importance of small commercial spaces in the local economy and offers strategic insights for stakeholders across various sectors.
#Interactive Sunburst Chart for Firm counts of Business Area Sizes occupied in Processed_Data_VOA
business_area_size_sunburst = px.sunburst(
Business_AreaSize_group_df,
path=[px.Constant('All'), 'AreaCombined_Group'],
values='Firm_count',
title='SUNBURST DISTRIBUTION:AREA SIZE OF ACCOMODATIONS OCCUPIED BY BUSINESSES IN PROCESSED_DATA_VOA'
)
business_area_size_sunburst.update_traces(root_color="lightgrey")
#display interactive plot
business_area_size_sunburst.show()
#Save the plot in the working folder
#Use write_image for saving plotly figures
business_area_size_sunburst.write_image('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\SUNBURST_DISTRIBUTION:AREA_SIZE_OF_ACCOMODATIONS_OCCUPIED_BY_BUSINESSES.png')
STATISTICAL SUMMARY ANALYSIS FOR INTERACTIVE VISUALIZATIONS OF FIRM COUNTS OF BUSINESS ACCOMODATIONS ACROSS DIFFERENT CATEGORIES IN PROCESSED_DATA_VOA.
This identifies the distribution and categorization of businesses in the dataset, using various interactive and descriptive visualizations to highlight key patterns and insights.
DATA CATEGORIZATION FUNCTIONS:
Categorize by Geography: this function groups the businesses based on their geographic locations by county and by local authority indicating a more specific region within a county.
The County Group shows counts of unique businesses within each county in the dataset displaying Essex as the county with the highest number of unique businesses, followed by smaller counts in Hertfordshire and Suffolk counties.
The Local Authority Group shows counts of unique businesses within each local authority in the dataset displaying Epping Forest and Harlow as local authorities with the highest concentrations of businesses in the dataset.
Categorize by Business Type: this function groups the businesses based on the business type such as offices, shops, warehouses, etc, showing the counts of unique businesses for each business type in the dataset. Workshops, offices, and warehouses are identified at the most common types of business accommodations in the Processed_Data_VOA dataset.
Categorize by Area Size Occupied: this function groups businesses based on the size of the area occupied by the type of business and the business accommodations and also counts the unique businesses for each combination of area size and business type.
INTERACTIVE VISUALIZATIONS:
Business Count by County:
This interactive horizontal bar chart for the number of unique businesses by county displays the number of unique businesses in each county in the Processed_Data_VOA dataset, sorting the counties from the highest to lowest number of businesses for better descriptive visualization.
Business Count by Local Authority:
This interactive horizontal bar chart for the category of businesses by the local authority in the Processed_Data_VOA dataset displays the number of unique businesses in each local authority, sorting the local authorities by the number of businesses to help identify areas with higher business concentrations.
Business Count by Property Type: This interactive bar chart distribution of frequencies across business property types in the Processed_Data_VOA dataset displays the frequency of different business property types that are the most common among businesses in the dataset.
Business Count by Area Size:
This interactive scatter plot displays the distribution of businesses by the size of the areas they occupy in the Processed_Data_VOA with each point representing a business type within a specific size category, giving insights into how different business types utilize space in the dataset from small area size categories to larger area sizes.
Treemap of Business Types:
This interactive treemap visualizes the distribution of the firm count of different business types in the Processed_Data_VOA dataset. Each rectangle represents a business type and the size of the rectangle corresponds to the number of business types.
Sunburst Chart of Business Area Sizes:
This interactive sunburst chart shows the distribution of business accommodations based on the size of the areas they occupy in the Processed_Data_VOA dataset which allows for a hierarchical view, starting from all businesses and then drilling do
All interactive plots are saved to the designated working environment.wn into more specific size categories.
DATA VISUALIZATION OF TOP 10 SELECTED INDUSTRIES IN LOCAL AUTHORITIES AND THEIR RESPECTIVE COUNTIES IN THE PROCESSED_DATA_VOA
#Plot businesses by local authorities grouping by local authority and business type
Local_authority_Business_type=Processed_Data_VOA.groupby(['Local_Authority', 'PriDescText'])['FirmName'].nunique().reset_index(name='Firm_count')
#Sort top 10 businesses to plot
Local_authority_Business_type=Local_authority_Business_type.sort_values(by='Firm_count', ascending=False).head(10)
#Merge the DataFrames
merged_data2 = pd.merge(Local_authority_Business_type, Local_authority_per_County, on='Local_Authority', how='left')
print(tabulate(merged_data2, headers='keys', tablefmt='psql'))
#Interactive bar chart
Barchart_Local_authority_Business_type = px.bar(
merged_data2,
x='Local_Authority',
y='Firm_count',
color='PriDescText',
title='BAR CHART OF TOP 10 SELECTED BUSINESSES BY LOCAL AUTHORITIES AND COUNTIES IN PROCESSED_DATA_VOA',
labels={'Firm_count': 'Number of Businesses', 'Local_Authority': 'Local Authority', 'PriDescText': 'Business Type'},
template='plotly_white',
hover_name='PriDescText',
hover_data={'County': True},
text='County',
color_discrete_sequence=px.colors.qualitative.Pastel1,
barmode='group'
)
#Update layout for the plot
Barchart_Local_authority_Business_type.update_layout(
xaxis_title='Local Authority',
yaxis_title='Number of Businesses',
showlegend=True,
legend_title_text='Business Types'
)
#Display interactive plot
Barchart_Local_authority_Business_type.show()
#Save the plot in the working folder
#Use write_image for saving plotly figures
Barchart_Local_authority_Business_type.write_image('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\BAR_CHART_OF_TOP_10_SELECTED_BUSINESSES_BY_LOCAL_AUTHORITIES_AND_COUNTIES .png')
+----+-------------------+------------------------+--------------+---------------+ | | Local_Authority | PriDescText | Firm_count | County | |----+-------------------+------------------------+--------------+---------------| | 0 | Epping Forest | Warehouse And Premises | 40 | ESSEX,HERTS | | 1 | Harlow | Workshop And Premises | 37 | ESSEX | | 2 | Epping Forest | Offices And Premises | 30 | ESSEX,HERTS | | 3 | Epping Forest | Workshop And Premises | 28 | ESSEX,HERTS | | 4 | Thurrock | Offices And Premises | 24 | ESSEX | | 5 | Harlow | Offices And Premises | 18 | ESSEX | | 6 | Uttlesford | Workshop And Premises | 17 | ESSEX,HERTS | | 7 | Chelmsford | Workshop And Premises | 15 | ESSEX | | 8 | Braintree | Offices And Premises | 13 | ESSEX,SUFFOLK | | 9 | Epping Forest | Store And Premises | 13 | ESSEX,HERTS | +----+-------------------+------------------------+--------------+---------------+
KEY INSIGHTS AND ANALYSIS: INTERACTIVE BAR CHART OF TOP 10 SELECTED BUSINESSES BY LOCAL AUTHORITIES AND BUSINESS TYPES IN THE PROCESSED_DATA_VOA DATASET
1.OVERVIEW OF BUSINESS DISTRIBUTION
Analysis: The interactive bar chart shows the distribution of the top 10 selected business counts across various local authorities and categorized by different business types.
The top 10 businesses are categorized into business types such as:
Warehouse and Premises Workshop and Premises Offices and Premises Store and Premises
The local authorities featured in the interactive chart are Epping Forest, Harlow, Uttlesford, Chelmsford, Thurrock, and Braintree.
Key Insight: Epping Forest and Harlow have the highest concentration of businesses among the selected local authorities in the dataset, particularly in the Workshop and Premises and Offices and Premises categories. This suggests that these areas may be hubs for specific types of industries, potentially linked to manufacturing, services, or retail sectors.
2.DOMINANCE OF SPECIFIC BUSINESS TYPES
Analysis: The interactive barchart reveals that Offices and Premises is the most frequent business type across multiple local authorities, particularly in Harlow, Chelmsford, and Braintree. Conversely, Store and Premises are less prevalent, indicating that fewer businesses in these regions operate under this category.
Key Insight: The predominance of Offices and Premises in this visualization, suggests a significant presence of service-oriented businesses or administrative headquarters in these regions. The lower numbers for Store and Premises might reflect a lower demand for retail space or a concentration of retail businesses in other areas not highlighted in this chart.
3.REGIONAL ECONOMIC SPECIALIZATION
Analysis: The local authorities of Epping Forest and Harlow show a diverse mix of business types, with significant numbers in both Workshop and Premises and Offices and Premises. Uttlesford local authority has a more balanced distribution across different business types, while Thurrock shows a strong presence in Offices and Premises only.
Key Insight: The diversity of business types in Epping Forest and Harlow indicates that these areas might be economically diverse, supporting a range of industries from manufacturing to services. Thurrock’s focus on Offices and Premises accommodations could suggest a specialization in professional services or corporate offices.
4.IMPLICATIONS FOR BUSINESS STRATEGY AND POLICIES
For Businesses: Companies looking to establish or expand in these regions can use this analysis to identify locations of business accommodations that align with their industry sector. For example, service-based companies might find Chelmsford or Harlow particularly attractive due to the concentration of Offices and Premises in these local authorities.
For Local Authorities: Understanding the concentration and type of businesses within their jurisdiction can help local authorities tailor their economic development strategies, zoning policies, and infrastructure investments to support existing industries and attract new businesses.
For Real Estate Developers: The demand for specific types of business accommodations, such as Offices and Premises or Workshops and Premises, highlights opportunities for real estate development in these sectors. Developers could focus on creating or modifying spaces that cater to these business needs.
DATA APPLICATIONS:
Economic Development: This analysis can guide local economic development initiatives to target industries that are underrepresented or overrepresented in different regions.
Business Relocation and Expansion: Companies can use these insights to make informed decisions about where to locate their operations based on the presence of similar or complementary businesses.
Urban Planning: The findings can inform zoning decisions, ensuring that adequate accommodations are available for different types of businesses and that infrastructure supports the predominant industries in each area.
This interactive bar chart visualization provides a detailed view of how business types are distributed across local authorities, offering critical insights into regional economic patterns and helping stakeholders make data-driven decisions.
STATISTICAL SUMMARY ANALYSIS FOR THE DATA VISUALIZATION OF THE TOP 10 SELECTED INDUSTRIES IN LOCAL AUTHORITIES AND THEIR RESPECTIVE COUNTIES IN THE PROCESSED_DATA_VOA DATASET.
write_image is used to save interactive plot to designated working environment.
Group by Local Authority and Business TypeT the dataset is grouped by Local_Authority such as Epping Forest, Harlow, Uttlesford, Chelmsford, Thurrock, and Braintree, and the PriDescText column is used for highlighting the business accommodations descriptions such as Warehouse and Premises, Workshop and Premises, Offices and Premises, Store, and Premises.
The number of unique businesses in each group is described and counted using the FirmName column, resulting in a new data frame named as Local_authority_Business_type.
Select Top 10 Business TTpes: the grouped DataFrame is sorted in descending order by the number of businesses in the dataset and the top 10 rows are selected to highlight the most common business types in each local authority.
Merge with CountT Data: the top 10 business types DataFrame is merged with another DataFrame that contains the counties corresponding to each local authority in the dataset creating merged_data2 data frame printed in a tabulated format to display and verify the top 10 business types, their counties, and their respective local authorities.
DATA VISUALIZATION: The interactive bar chart shows the number of businesses for the top 10 business types in various local authorities in the Processed_Data_VOA dataset with each bar representing a business type within a local authority.
This interactive and descriptive visualization plot helps to understand the distribution of various business types and accommodations across different local authorities and their respective counties in the dataset, highlighting regions with higher concentrations of certain business activities.
Different colors are used for different business types, making it easy to distinguish between businesses, and descriptive information about the counties of each local authority is included in the interactive plot.
Key insights from the interactive bar chart: Epping Forest has the highest number of businesses in "Warehouse and Premises" and "Offices and Premises." Harlow has a significant number of "Workshop and Premises." Thurrock and Chelmsford als"bers of "Offices and Premises" and "WorkshopPROCESSED_DATA_VOA
DATA VISUALIZATION TO IDENTIFY THE LONGEST OCCUPANCY OF BUSINESS ACCOMODATION IN THE PROCESSED_DATA_VOA DATASET:
#Display unique values in the 'FirmName' column of the dataset
unique_firm_names = Processed_Data_VOA['FirmName'].unique()
#Print all unique values of firm names of businesses including empty strings present in the Processed_data_VOA dataset
for name in unique_firm_names:
print(name)
The Occupier SECURITY SEAL TECHNOLOGY D C POULTON AND SONS THE FINISHING LINE SOLO SPRAYERS LTD WOOD & FAMILY GARAGE UNIT 6 DUNMOW FENCING SUPPLIES LTD TOTAL BATHROOMS LTD S T C INSTRUMENT SERVICES STANSTED HSE MANAGEMENT LTD LIFTRAFT HOUSE TOP TRUCKS D D S M TOOLS LTD P G OXLEY GAMET PRODUCTS LTD BATEMAN MOULD & TOOL LTD W E MARSON & CO LTD CHELMSFORD AUCTION ROOMS IBC HOUSE PETER ABBOTT ILFORD ENG UNIBAR H & E EDWARDS D PERFECT & SONS LTD SWIFTCLEAN CHELMSFORD BOROUGH COUNCIL C K BATTEN BUILDERS DANBURY FENCING JOHN HOLMAN & SONS LTD WRAGG BROTHERS LTD IPB MOTOR SERVICES GM BRITTON (PUBLIC WORKS) LTD LONDON FITTINGS OLYMPIAN COACHES LTD BIGODS HALL UNIT 4 GARAGE UNIT 22 UNIT 23 GALLIARD HOMES COLES COPYING UNIT 1 ADJ 197 SAFFRON WALDEN STEAM LAUNDRY THE METAL BARN ATLAS REPAIRS LTD E A WALFORD LABEL HOUSE WREN PRINTED PACKAGING WILSON H W LTD 1 GEO HARKER & CO 3 J CHALLIS & SON TRUCK SERVICES PPE LTD GTA STUDIOS EASTON SHEET METAL LIMITED MOISE DREYFUS F S FOODS LTD EASIFLOOR LTD T J AUTOS SUITE B2 ASPECTS CONTRACTS LTD AUMAC LTD HARLOW AUTO AIR CONDITIONING PEPPER & TOMLIN LTD HARLOW AUTO CENTRE HARLOW AM2020 LIMITED ROMANS AUTO MINJ MOTS BRAINTREE ENGINEERING CO LTD HOBSON & SONS LTD ZEELANDIA (H J DOELEMAN LTD) COTTIS & SON TRANSPORT LTD UNIT 15B UNIT 3 UNIT 4 UNIT 13 UNIT 9 UNIT 15A UNIT 2/2A UNIT 12 UNIT 24 WASTE-A-WAY RECYCLING LTD EC2I FADULLA LTD ST ANNS MANUFACTURING CO LTD ENVIROTEK BM STYLING LIMITED SELECT CAR RENTALS THE ESSEX CRC PURFLEET TRUCK PARK LAWLOR CAR SERVICE CITIZENS ADVICE BUREAU EX PURDEYS TIMBER CO LTD AMBROSE AUCTIONEERS & VALUERS UNIT 18 ADJ UNIT 15B SECURITY OFFICE DOHERTY CONSTRUCTION LTD UNIT 3/4 FREEPORT OFFICE VILLAGE HOLMES & HILLS SOLICITORS CORY ENVIRONMENTAL MAGNUM LOGISTICS TRANSGLOBAL SHIELDS ENERGY SERVICES HARVEY COPPING & HARRISON T D C STORAGE AREA 1 LIME HOUSE NURSERY HAART VECTA AT H G EVANS ECC HIGHWAYS IVY HOUSE H S SERVICES BAWTREE & SONS WALTHAM ABBEY TOWN COUNCIL ELLISON A C SPOONER CROWN FASHIONS HARLOW DISTRICT COUNCIL TRINITY SERVICE STATION L C DAVIS G M BRITON LTD KINGS TRANSPORT SERVICES TES HOUSE JONES & SON SUN MOTORS ENGLISH ELECTRIC VALVE CO AJ SMITH TIMBER LTD 1ST FLOOR TRANSWASTE CONTAINERS UNIT 21E REAR 1ST FLR UNIT 4B DEEP CLEAN SERVICES WERU (UK) LTD UNIT S5 GND FLR UNIT 4A JAMES HORNSBY SCHOOL SOUTH EAST ESSEX TECHNOLOGY CENTRE HOCKLEY POSTMENS SORTING OFFICE MALDON ARCHAEOLOGICAL GROUP GREENS STORES SAUNDERS F AND PARTNERS ROCK & ALUVIUM LTD CAPRON T A AND CO NATIONWIDE BUILDING SOCIETY POST OFFICE SERVICES STH OCKENDON POSTMANS OFFICE Q N HOUSE BRIGHTCAST GROUP SERVICES LTD R & J SCAFFOLDING LTD MAIN BUILDING MAINTENANCE LTD PORT HEALTH AUTHORITY KIRWIN & SIMPSON SUITE OWS CUNNINGTON SON & ORFLEUR MERVYN BEECHAM & BODIAM WOODYS PEAT BARRON ROWLES & BASS ASHBEE & CO NELSON MITCHELL & WILLIAMS FROST & CO MODERN ALARMS WARWICK HOUSE CANOE CLUB WE BUY ANY CAR.COM LONDON FOOD AND DRINKS LTD MALDON CARNIVAL ASSOCIATION WOMENS ROYAL VOLUNTARY SERVICE STOBART ROGERS AUTOS CALLAWAY AND SONS INSURANCE CONSULTANTS GB ADAPTIONS LTD EA VEHICLES LTD WESLEY JAMES LTD ARC CONSTRUCTION LTD TYRE FOX BG ACCOUNTING LTD ROMFORD CHILLED TRANSPORT ESSEX COUNTY PARKING LTD AMBASSADOR CRUISE LINE TRANSEND GRANGE FENCING DA GAS HEATING AND PLUMBING LTD 1 STOP REC GILBARCO LTD HORSE RANGERS ASSOCIATION TITAN BOXTAINER CARTER & WARD OF WICKFORD LTD UNIT 21A/B THAMESIDE TAXIS LTD CML MICROSYSTEMS TOTAL HEADTURNERS WARDENS OFFICE CHEP UK LTD AUTOFACTORS LTD STORT SCAFFOLDING COMPANY C.P.D. EUROPE BRANDONS COACHES D & R LANDSCAPES BASKET WORKS RURAL BUSINESS PARK EPPING FOREST PISTOL CLUB MBS GROUNDS MAINTAINANCE D J PEGRAM & SONS CARAVAN SITE DANBURY HAULAGE THE ELECTRIC INCENTIVE CO ORCHARD GARAGE ENVIRONTEC LTD GLIBBERIES SVP CAR PARK SMITH & HOWELL LONGDENS TRANSPORT WYNTER FARM BARN AT STABLES WYNTER FARM AT SPEEDWELL MOTORS ELSENHAM WATER LTD THE OLD STABLES OFFICE WINSPACE LTD AT EPPING MOTOR COMPANY BRENTWOOD AUTOSPARES INTERSTYLE GROUP LTD REGENT GARAGE CORDER D M ASSOCIATES L J ANDERSON & SONS HARTLEY & BROOKS BOAT DESIGNERS LTD HARTLEY & BROOKS BOAT DESIGN LTD PORT FLAIR LTD B WHITING TOLLESBURY SALTINGS LTD ESTUARY CITRUS TRAINING CAPITAL PLANT LTD E-TOILET SERVICES LTD SECTION B GALLOPER WIND FARM LTD SECTION A SECTION C BACK ASSETS TAG FARNBOROUGH ALFIE BEST PROPERTY GROWTH STANSTED AEROSPACE NDS VOICE COMMUNICATIONS VAAROOM UK PROPERTIES MANAGEMENT UNIT 3 BURY FARM UNIT 19 BURY FARM UNIT 20 BURY FARM UNIT 1 BURY FARM UNIT 2 BURY FARM BAE SYSTEMS GARDNER TRAVEL VACANT OFFICES COACH STOP COBB SCAFFOLDING WESTBOUND SHIPPING LTD JOHN GOOD SHIPPING WINCANTON GROUP LTD STEDMAN GROUP OF COMPANIES A1 SCHMITZ CARGOBULL LTD P & B MOTORS SET IN STONE SHOEBURYNESS SORTING OFFICE R & Y TYERS REMOVALS WYVERN ENGINEERING SERVICE KNSI FOREMOST RESIN PROJECTS LTD DK STORAGE LUXURY TOILET HIRE UK LTD WESTONS PAVING SLABS CLARE NURSERY WESTSIDE DAVIDSON TRADING CO LTD KINGS (I P) LTD WESTON SIMFIRE CHANDELIER CLEANING SERVICES LTD R EMERY E J KENNERLEY & SON G W SMITH & SONS (BATTLESBRIDGE) LTD T BOATS CHELMSFORD CAR VALET SERVICE JEM ENGINEERING BARRY IVES HAULAGE LTD FAIRLOP CONCRETE T HAMMOND T R PRECISION ENGINEERING STUDIO ONE FRAMES LTD SAXON CONTRACTS LTD UNIT 1 UNIT 10 UNITS 19-20 LICNACITE (NORTH LONDON) LTD AJIMA LTD UNIT 5 A J DYKE & SONS LTD UNIT 2 UNIT 7 UNIT 11 UNIT 11B UNIT 2A KINGSTON FARM UNIT 11A DICOL UK AD THEOBOLD & SON WINE FANTASTIC ART GARDEN CENTRE BERNERS HALL FARM VALCO UK ENGINEERING LTD FORDS COACHES GREAT TOTHAM GARAGE CHAMOIS MOTORS LIBRA GRAPHIC DESIGN LIFESTILES LTD TRANSPORTER ENGINEERING LTD WILKIN & SONS LTD LIVERY AND ATKINSON LTD UNIT 8 ACRON TRUCK ACTION VEHICLES LTD TAYLOR BESPOKE LIMITED ALVANT MANAGEMENT LTD TAG AVIATION NORMAN ENGINEERING NORTH WEALD FLYING SERVICES LTD CYBERDERMIS R WOOD MOWERS UNITS 3 & 4 SMC TRADING A.WYLIE LODGE COACHES MMP EC GRAPHICS DANIEL CHISHOLM GROUNDWORKS CONFETTI & LACE ARRK EUROPE LTD NORTH WEALD SAW SERVICES ADVANCED TUITION CAR & SKID TRAINING SCHOOL LA ELECTRONICS ASSOCIATED JOINERY TECHNIQUES HARJO LTD THE TACK ROOM OFFICE THE BULL PEN OFFICE YOUR CHOICE BEE HOUSE N I READMAN MEATLINE LTD ELLIS CONTRACTORS 4 UTILITIES CLEANAWAY LTD THE COMMODITY CENTRE SERVICE TEAM ROCHFORD DISTRICT COUNCIL ANGLIAN AUTO RECOVERY PENMAN & GORE STATCO ELECTRONICS REALCOURT CONSTRUCTION LTD B H DENTAL LAB LTD UNITS S6 S7 S8 & S9 UNIT F17 UNITS S12 & S13 ACIT SOLUTIONS UNIT F19 HOLLAND PLASTICS LTD CKR SERVICES DIRECT MOVES DISTRICT BUS CO LOMBARD NORTH CENTRAL PLC S C GROVER LTD RECEPTION AREA + CONFERENCE SUITES GLOBAL GOLD MERIAL ANIMAL HEALTH INTERSIL COURTLAND WASTE MANAGEMENT MEDISECURE LIMITED WOLFELEC LIMITED GA PRECISION PRODUCTS ATS TYRES PAPER PAK GROVER TRANSPORT EBME LIMITED FABER & FABER LTD R F A SYSTEMS LTD B B & C FENCING LTD LIGHTER LIFE LIMITED SWR MOTORSPORTS RAMAR ENGINEERING LTD L S A HOLDINGS LTD EASTERN HARDWOODS LTD CROWN BROLAC INTERLINK A1 BACON CO LTD CD OFFICE INTERIORS LIMITED CJ PLUMBING AND HEATING LIMITED GBM BUILDING SERVICES LIMITED THERMIONIC CULTURE LIMITED CARDIOMETRIC LIMITED HOWARD AND BUCKNER PLUMBING SERVICES JS NETWORK SOLUTIONS ANGELIC QUALITY SYSTEMS LIMITED CONTROL COMPONENTS ELLIES SNACK BAR BROOKFIELD VISCOMETERS DAVID GREEN STUDIOS MGM COMPUTING LIMITED CARE AND INDEPENDENCE LIMITED COIN IT LEISURE LIMITED TATEX ENGINEERING LIMITED DIRECT ELECTRICAL WHOLESALE LIMITED F STOPS CARPET STUDIO QUEST CANCER RESEARCH HOWARD AND BUCKNER PLUMBING SERVICES LIMITED AMBER A/C ELECTRICAL LIMITED MOLECULAR PRODUCTS LTD DAVID BROOKS DJ JORDON WALL AND FLOOR TILING AEC LIMITED MARLBOROUGH SURFACING LAND NORTH OF TARMACADAM PLANT FARM BUILDINGS UNIT 8B THE GARAGE S J RAYMENT ACCIDENT AND HEALTH UNDERWRITING LTD S & D PLANT & COMMERCIAL REPAIRS FINANCIAL VISION ACE OPTIONS VACANT PART CAPITALCLIFF LIMITED EUROSHUTTERS GO EXPRESS ABC TRANSMISSIONS THERMONIC CULTURE LIMITED ROGA KOPTYA LTD R & S AUTOS REPAIR & REFURB ABS TYRES ROVER TECH IPECO HOLDINGS EJS MOTORS H BALLARD WEST STREET VEHICLE DISMANTLERS S R TECHNICS ARGENT BUILDERS STORM AVIATION PLC HUNWICK LTD G M TRANSPORT (WALTHAM ABBEY) DOONES YARD MANSELL BONDED FABRICS H C R ELECTRONICS CLIFFS AUTOS GENERAL PORTFOLIO EVERARD OVENDON & CO LTD THE TRANSFORMER & ELECTRICAL CO LTD WILLIAM H JUDD & CO LTD PREMIER MOVING SERVICES MR A. SAMPSON UDC PLOT 12 UDC PLOT 14 STANFORD COACHWORKS & MOBILITY SERVICES LTD HALL & CO BUILDING MATERIALS CENTRE FUELLING SUPPORT LTD WESTBURY CONSERVATORIES C A BLACKWELL (CONTRACTS) AUDIX B B PORT LANE GARAGE SOUTH ESSEX STOCKHOLDERS LTD WOODLAND DC MORGAN & CO LTD BRAEFIELD PRECISION ENGINEERS TELTRONIC COMM LTD F J FRENCH LTD HARVEY BUSINESS PARK ERNEST DOE AND SONS SANDALS MOTORS (ESSEX) LTD NEW CONCEPT INTERIORS LTD BP OIL UK LTD BOSAL UK LTD SBC BLUNDEL WOODWORKER MACHINERY WILKS RUBBER PLASTICS LTD P H S LTD WALLEY LTD RUSH PARCEL SERVICES C M C FABRICATIONS TESCO PLC JET ONE X JEN'S AUTOS STOCK AUTO BREAKERS TEMPLE FARM SPARES MESSRS ASH KHAN & CO BLACK CROUCHMAN & CO TENANTS ASSOCIATION KINGSLEY (GRAYS) LTD JESSOPP AND GOUGH THE RONA PARTNERSHIP ACG PUBLICATIONS COLCHESTER CO-OPERATIVE SOCIETY LEE VALLEY REGIONAL PARK AUTHORITY CHARRINGTONS SOLID FUEL R HUTCHINGS MACS BUS HIRE TRINITY HOUSE LIGHTHOUSE SERVICE THE PARISH CLERK CIS LTD UDC PLOT 42 BARTLETT HAMMOND LTD COUNTY LIQUIDATIONS READY MIXED CONCRETE BATES TRAVELL SOLICITORS K TRAINING A B S INSURANCE AGENCY LTD UNIT 19C REAR MR PHIL TERRY SIMMONS AND SON GREAT EASTON FILLING STATION THE HILL COMPANY LTD A G CLAYDEN CENTURY HOUSE CMN LOGISTICS LTD LITTLEHAMPTON BOOK SERVICES LTD PROCTOR AND GAMBLE LTD OFFICES AT 2ND FLR RAEBURN HOUSE 1-3 S D SAMUELS LTD T G PENMAN & CO ST MODWEN PROPERTIES PLC OFFICE B HEAVYLIFT CARGO CHICK PRINTERS NETWORK 81 ECHO NEWSPAPERS YACHTING PRESS (VACANT) DEAD OR ALIVE MID ESSEX MIND
#group and categorize firm names for better readability
list_unique_firm_names = [
'The Occupier','SECURITY SEAL TECHNOLOGY','UNIT 6','CHUNKY CHICKEN','(VACANT)','UNIT 4','STANSTED AEROSPACE','EASTON SHEET METAL LIMITED',
'THE METAL BARN','HARLOW DISTRICT COUNCIL','NDS','LONDON FITTINGS','T J AUTOS','UNIT 4 GARAGE','BAE SYSTEMS','COLES COPYING','S T C INSTRUMENT SERVICES',
'UNIT 3','TOTAL BATHROOMS LTD','UNIBAR','THE FINISHING LINE','GEO HARKER & CO','1','3','P G OXLEY','SAFFRON WALDEN STEAM LAUNDRY','PETER ABBOTT',
'UNIT 22','D D S M TOOLS LTD','WOOD & FAMILY GARAGE','ILFORD ENG','D C POULTON AND SONS','CHELMSFORD BOROUGH COUNCIL','W E MARSON & CO LTD',
'1ST FLOOR','WILSON H W LTD','GALLIARD HOMES','UNIT 23','TOP TRUCKS','MAGNUM LOGISTICS','H & E EDWARDS','BIGODS HALL','ATLAS REPAIRS LTD',
'SWIFTCLEAN','IPB MOTOR SERVICES','GM BRITTON (PUBLIC WORKS) LTD','OLYMPIAN COACHES LTD','DANBURY FENCING','WRAGG BROTHERS LTD','LABEL HOUSE',
'UNIT 1 ADJ 197','UNIT 21E REAR','SUITE B2','ASPECTS CONTRACTS LTD','HARLOW AUTO CENTRE','PEPPER & TOMLIN LTD','HARLOW AM2020 LIMITED','ROMANS AUTO',
'MINJ MOTS','BRAINTREE ENGINEERING CO LTD','HOBSON & SONS LTD','ZEELANDIA (H J DOELEMAN LTD)','COTTIS & SON TRANSPORT LTD','UNIT 15B','UNIT 3/4 FREEPORT OFFICE VILLAGE',
'HOLMES & HILLS SOLICITORS','CORY ENVIRONMENTAL','SHIELDS ENERGY SERVICES','STORAGE AREA 1 LIME HOUSE NURSERY','HAART','VECTA AT','H G EVANS','ECC HIGHWAYS',
'IVY HOUSE','H S SERVICES','BAWTREE & SONS','WALTHAM ABBEY TOWN COUNCIL','ELLISON','A C SPOONER','CROWN FASHIONS','TRINITY SERVICE STATION','L C DAVIS',
'G M BRITON LTD','KINGS TRANSPORT SERVICES','TES HOUSE','JONES & SON','SUN MOTORS','ENGLISH ELECTRIC VALVE CO','AJ SMITH TIMBER LTD','1ST FLOOR',
'TRANSWASTE CONTAINERS','UNIT 21A/B','THAMESIDE TAXIS LTD','CML MICROSYSTEMS','TOTAL HEADTURNERS','WARDENS OFFICE','CHEP UK LTD','AUTOFACTORS LTD',
'STORT SCAFFOLDING COMPANY','C.P.D. EUROPE','BRANDONS COACHES','D & R LANDSCAPES BASKET WORKS RURAL BUSINESS PARK','EPPING FOREST PISTOL CLUB','MBS GROUNDS MAINTAINANCE',
'D J PEGRAM & SONS','CARAVAN SITE','DANBURY HAULAGE','THE ELECTRIC INCENTIVE CO','ORCHARD GARAGE','ENVIRONTEC LTD','GLIBBERIES','SVP CAR PARK','SMITH & HOWELL',
'LONGDENS TRANSPORT','WYNTER FARM BARN AT','STABLES WYNTER FARM AT','SPEEDWELL MOTORS','ELSENHAM WATER LTD','THE OLD STABLES OFFICE','WINSPACE LTD AT',
'EPPING MOTOR COMPANY','BRENTWOOD AUTOSPARES','INTERSTYLE GROUP LTD','REGENT GARAGE','CORDER D M ASSOCIATES','L J ANDERSON & SONS','HARTLEY & BROOKS BOAT DESIGNERS LTD',
'HARTLEY & BROOKS BOAT DESIGN LTD','PORT FLAIR LTD','B WHITING','TOLLESBURY SALTINGS LTD','ESTUARY','CITRUS TRAINING','CAPITAL PLANT LTD','E-TOILET SERVICES LTD',
'SECTION B','GALLOPER WIND FARM LTD','SECTION A','SECTION C','BACK ASSETS','TAG FARNBOROUGH','ALFIE BEST PROPERTY GROWTH','STANSTED AEROSPACE','NDS',
'VOICE COMMUNICATIONS','VAAROOM','UK PROPERTIES MANAGEMENT','UNIT 3 BURY FARM','UNIT 19 BURY FARM','UNIT 20 BURY FARM','UNIT 1 BURY FARM','UNIT 2 BURY FARM',
'BAE SYSTEMS','GARDNER TRAVEL','VACANT OFFICES','COACH STOP','COBB SCAFFOLDING','WESTBOUND SHIPPING LTD','JOHN GOOD SHIPPING','WINCANTON GROUP LTD',
'STEDMAN GROUP OF COMPANIES','A1 SCHMITZ CARGOBULL LTD','P & B MOTORS','SET IN STONE','SHOEBURYNESS SORTING OFFICE','R & Y TYERS REMOVALS','WYVERN ENGINEERING SERVICE',
'KNSI','FOREMOST RESIN PROJECTS LTD','DK STORAGE','LUXURY TOILET HIRE UK LTD','WESTONS PAVING SLABS','CLARE NURSERY','WESTSIDE DAVIDSON TRADING CO LTD',
'KINGS (I P) LTD','WESTON SIMFIRE','CHANDELIER CLEANING SERVICES LTD','R EMERY','E J KENNERLEY & SON','G W SMITH & SONS (BATTLESBRIDGE) LTD','T BOATS',
'CHELMSFORD CAR VALET SERVICE','JEM ENGINEERING','BARRY IVES HAULAGE LTD','FAIRLOP CONCRETE','T HAMMOND','T R PRECISION ENGINEERING','STUDIO ONE FRAMES LTD',
'SAXON CONTRACTS LTD','UNIT 1','UNIT 10','UNITS 19-20','LICNACITE (NORTH LONDON) LTD','AJIMA LTD','UNIT 5','A J DYKE & SONS LTD','UNIT 2','UNIT 7',
'UNIT 11','UNIT 11B','UNIT 2A KINGSTON FARM','UNIT 11A','DICOL UK','AD THEOBOLD & SON','WINE FANTASTIC','ART GARDEN CENTRE','BERNERS HALL FARM','VALCO UK ENGINEERING LTD',
'FORDS COACHES','GREAT TOTHAM GARAGE','CHAMOIS MOTORS','LIBRA GRAPHIC DESIGN','LIFESTILES LTD','TRANSPORTER ENGINEERING LTD','WILKIN & SONS LTD','LIVERY AND ATKINSON LTD',
'UNIT 8','ACRON TRUCK','ACTION VEHICLES LTD','TAYLOR BESPOKE LIMITED','ALVANT MANAGEMENT LTD','TAG AVIATION','NORMAN ENGINEERING','NORTH WEALD FLYING SERVICES LTD',
'CYBERDERMIS','R WOOD MOWERS','UNITS 3 & 4','SMC TRADING', 'A.WYLIE','LODGE COACHES','MMP','EC GRAPHICS','DANIEL CHISHOLM GROUNDWORKS','CONFETTI & LACE',
'ARRK EUROPE LTD','NORTH WEALD SAW SERVICES','ADVANCED TUITION CAR & SKID TRAINING SCHOOL','LA ELECTRONICS','ASSOCIATED JOINERY TECHNIQUES','HARJO LTD',
'THE TACK ROOM OFFICE','THE BULL PEN OFFICE','YOUR CHOICE','BEE HOUSE','N I READMAN','MEATLINE LTD','ELLIS CONTRACTORS','4 UTILITIES','CLEANAWAY LTD',
'THE COMMODITY CENTRE','SERVICE TEAM','ROCHFORD DISTRICT COUNCIL','ANGLIAN AUTO RECOVERY','PENMAN & GORE','STATCO ELECTRONICS','REALCOURT CONSTRUCTION LTD',
'B H DENTAL LAB LTD','UNITS S6 S7 S8 & S9','UNIT F17','UNITS S12 & S13','ACIT SOLUTIONS','UNIT F19','HOLLAND PLASTICS LTD','CKR SERVICES','DIRECT MOVES',
'DISTRICT BUS CO','LOMBARD NORTH CENTRAL PLC','S C GROVER LTD','RECEPTION AREA + CONFERENCE SUITES','GLOBAL GOLD','MERIAL ANIMAL HEALTH','INTERSIL',
'COURTLAND WASTE MANAGEMENT','MEDISECURE LIMITED','WOLFELEC LIMITED','GA PRECISION PRODUCTS','ATS TYRES','PAPER PAK','GROVER TRANSPORT','EBME LIMITED',
'FABER & FABER LTD','R F A SYSTEMS LTD','B B & C FENCING LTD','LIGHTER LIFE LIMITED','SWR MOTORSPORTS','RAMAR ENGINEERING LTD','L S A HOLDINGS LTD',
'EASTERN HARDWOODS LTD','CROWN BROLAC','INTERLINK','A1 BACON CO LTD','CD OFFICE INTERIORS LIMITED','CJ PL NG','AEC LIMITED','MARLBOROUGH SURFACING','LAND NORTH OF TARMACADAM PLANT',
'FARM BUILDINGS','UNIT 8B','THE GARAGE','S J RAYMENT','ACCIDENT AND HEALTH UNDERWRITING LTD','S & D PLANT & COMMERCIAL REPAIRS','FINANCIAL VISION',
'ACE OPTIONS','VACANT PART','CAPITALCLIFF LIMITED','EUROSHUTTERS','GO EXPRESS','ABC TRANSMISSIONS','THERMONIC CULTURE LIMITED','ROGA KOPTYA LTD','R & S AUTOS',
'REPAIR & REFURB','ABS TYRES','ROVER TECH','IPECO HOLDINGS','EJS MOTORS','H BALLARD','WEST STREET VEHICLE DISMANTLERS','S R TECHNICS','ARGENT BUILDERS',
'STORM AVIATION','PLC HUNWICK LTD','G M TRANSPORT (WALTHAM ABBEY)','DOONES YARD','MANSELL BONDED FABRICS','H C R ELECTRONICS','CLIFFS AUTOS','GENERAL PORTFOLIO',
'EVERARD OVENDON & CO LTD','THE TRANSFORMER & ELECTRICAL CO LTD','WILLIAM H JUDD & CO LTD','PREMIER MOVING SERVICES','MR A. SAMPSON','UDC PLOT 12',
'UDC PLOT 14','STANFORD COACHWORKS & MOBILITY SERVICES LTD','HALL & CO BUILDING MATERIALS CENTRE','FUELLING SUPPORT LTD','WESTBURY CONSERVATORIES',
'C A BLACKWELL (CONTRACTS)','AUDIX B B','PORT LANE GARAGE','SOUTH ESSEX STOCKHOLDERS LTD','WOODLAND','DC MORGAN & CO LTD','BRAEFIELD PRECISION ENGINEERS',
'TELTRONIC COMM LTD','F J FRENCH LTD','HARVEY BUSINESS PARK','ERNEST DOE AND SONS','SANDALS MOTORS (ESSEX) LTD','NEW CONCEPT INTERIORS LTD','BP OIL UK LTD',
'BOSAL UK LTD','SBC','BLUNDEL WOODWORKER MACHINERY','WILKS RUBBER PLASTICS LTD','P H S LTD','WALLEY LTD','RUSH PARCEL SERVICES','C M C FABRICATIONS',
'TESCO PLC','JET ONE X','JENS AUTOS','STOCK AUTO BREAKERS','TEMPLE FARM SPARES','MESSRS ASH KHAN & CO','BLACK CROUCHMAN & CO','TENANTS ASSOCIATION',
'KINGSLEY (GRAYS) LTD','JESSOPP AND GOUGH','THE RONA PARTNERSHIP','ACG PUBLICATIONS','COLCHESTER CO-OPERATIVE SOCIETY',
'LEE VALLEY REGIONAL PARK AUTHORITY','CHARRINGTONS SOLID FUEL','R HUTCHINGS','MACS BUS HIRE','TRINITY HOUSE LIGHTHOUSE SERVICE','THE PARISH CLERK',
'CIS LTD','UDC PLOT 42','BARTLETT HAMMOND LTD','COUNTY LIQUIDATIONS','READY MIXED CONCRETE','BATES TRAVELL SOLICITORS','K TRAINING','A B S INSURANCE AGENCY LTD',
'UNIT 19C REAR','MR PHIL TERRY','SIMMONS AND SON','GREAT EASTON FILLING STATION','THE HILL COMPANY LTD','A G CLAYDEN','CENTURY HOUSE','CMN LOGISTICS LTD',
'LITTLEHAMPTON BOOK SERVICES LTD','PROCTOR AND GAMBLE LTD','OFFICES AT 2ND FLR RAEBURN HOUSE 1-3','S D SAMUELS LTD','T G PENMAN & CO','ST MODWEN PROPERTIES PLC',
'OFFICE B','HEAVYLIFT CARGO','CHICK PRINTERS','NETWORK 81','ECHO NEWSPAPERS','YACHTING PRESS','(VACANT)','DEAD OR ALIVE','MID ESSEX MIND'
]
#Convert list to DataFrame
df_unique_firm_names = pd.DataFrame(list_unique_firm_names, columns=['FirmName'])
print(tabulate(df_unique_firm_names, headers='keys', tablefmt='psql'))
+-----+---------------------------------------------------+ | | FirmName | |-----+---------------------------------------------------| | 0 | The Occupier | | 1 | SECURITY SEAL TECHNOLOGY | | 2 | UNIT 6 | | 3 | CHUNKY CHICKEN | | 4 | (VACANT) | | 5 | UNIT 4 | | 6 | STANSTED AEROSPACE | | 7 | EASTON SHEET METAL LIMITED | | 8 | THE METAL BARN | | 9 | HARLOW DISTRICT COUNCIL | | 10 | NDS | | 11 | LONDON FITTINGS | | 12 | T J AUTOS | | 13 | UNIT 4 GARAGE | | 14 | BAE SYSTEMS | | 15 | COLES COPYING | | 16 | S T C INSTRUMENT SERVICES | | 17 | UNIT 3 | | 18 | TOTAL BATHROOMS LTD | | 19 | UNIBAR | | 20 | THE FINISHING LINE | | 21 | GEO HARKER & CO | | 22 | 1 | | 23 | 3 | | 24 | P G OXLEY | | 25 | SAFFRON WALDEN STEAM LAUNDRY | | 26 | PETER ABBOTT | | 27 | UNIT 22 | | 28 | D D S M TOOLS LTD | | 29 | WOOD & FAMILY GARAGE | | 30 | ILFORD ENG | | 31 | D C POULTON AND SONS | | 32 | CHELMSFORD BOROUGH COUNCIL | | 33 | W E MARSON & CO LTD | | 34 | 1ST FLOOR | | 35 | WILSON H W LTD | | 36 | GALLIARD HOMES | | 37 | UNIT 23 | | 38 | TOP TRUCKS | | 39 | MAGNUM LOGISTICS | | 40 | H & E EDWARDS | | 41 | BIGODS HALL | | 42 | ATLAS REPAIRS LTD | | 43 | SWIFTCLEAN | | 44 | IPB MOTOR SERVICES | | 45 | GM BRITTON (PUBLIC WORKS) LTD | | 46 | OLYMPIAN COACHES LTD | | 47 | DANBURY FENCING | | 48 | WRAGG BROTHERS LTD | | 49 | LABEL HOUSE | | 50 | UNIT 1 ADJ 197 | | 51 | UNIT 21E REAR | | 52 | SUITE B2 | | 53 | ASPECTS CONTRACTS LTD | | 54 | HARLOW AUTO CENTRE | | 55 | PEPPER & TOMLIN LTD | | 56 | HARLOW AM2020 LIMITED | | 57 | ROMANS AUTO | | 58 | MINJ MOTS | | 59 | BRAINTREE ENGINEERING CO LTD | | 60 | HOBSON & SONS LTD | | 61 | ZEELANDIA (H J DOELEMAN LTD) | | 62 | COTTIS & SON TRANSPORT LTD | | 63 | UNIT 15B | | 64 | UNIT 3/4 FREEPORT OFFICE VILLAGE | | 65 | HOLMES & HILLS SOLICITORS | | 66 | CORY ENVIRONMENTAL | | 67 | SHIELDS ENERGY SERVICES | | 68 | STORAGE AREA 1 LIME HOUSE NURSERY | | 69 | HAART | | 70 | VECTA AT | | 71 | H G EVANS | | 72 | ECC HIGHWAYS | | 73 | IVY HOUSE | | 74 | H S SERVICES | | 75 | BAWTREE & SONS | | 76 | WALTHAM ABBEY TOWN COUNCIL | | 77 | ELLISON | | 78 | A C SPOONER | | 79 | CROWN FASHIONS | | 80 | TRINITY SERVICE STATION | | 81 | L C DAVIS | | 82 | G M BRITON LTD | | 83 | KINGS TRANSPORT SERVICES | | 84 | TES HOUSE | | 85 | JONES & SON | | 86 | SUN MOTORS | | 87 | ENGLISH ELECTRIC VALVE CO | | 88 | AJ SMITH TIMBER LTD | | 89 | 1ST FLOOR | | 90 | TRANSWASTE CONTAINERS | | 91 | UNIT 21A/B | | 92 | THAMESIDE TAXIS LTD | | 93 | CML MICROSYSTEMS | | 94 | TOTAL HEADTURNERS | | 95 | WARDENS OFFICE | | 96 | CHEP UK LTD | | 97 | AUTOFACTORS LTD | | 98 | STORT SCAFFOLDING COMPANY | | 99 | C.P.D. EUROPE | | 100 | BRANDONS COACHES | | 101 | D & R LANDSCAPES BASKET WORKS RURAL BUSINESS PARK | | 102 | EPPING FOREST PISTOL CLUB | | 103 | MBS GROUNDS MAINTAINANCE | | 104 | D J PEGRAM & SONS | | 105 | CARAVAN SITE | | 106 | DANBURY HAULAGE | | 107 | THE ELECTRIC INCENTIVE CO | | 108 | ORCHARD GARAGE | | 109 | ENVIRONTEC LTD | | 110 | GLIBBERIES | | 111 | SVP CAR PARK | | 112 | SMITH & HOWELL | | 113 | LONGDENS TRANSPORT | | 114 | WYNTER FARM BARN AT | | 115 | STABLES WYNTER FARM AT | | 116 | SPEEDWELL MOTORS | | 117 | ELSENHAM WATER LTD | | 118 | THE OLD STABLES OFFICE | | 119 | WINSPACE LTD AT | | 120 | EPPING MOTOR COMPANY | | 121 | BRENTWOOD AUTOSPARES | | 122 | INTERSTYLE GROUP LTD | | 123 | REGENT GARAGE | | 124 | CORDER D M ASSOCIATES | | 125 | L J ANDERSON & SONS | | 126 | HARTLEY & BROOKS BOAT DESIGNERS LTD | | 127 | HARTLEY & BROOKS BOAT DESIGN LTD | | 128 | PORT FLAIR LTD | | 129 | B WHITING | | 130 | TOLLESBURY SALTINGS LTD | | 131 | ESTUARY | | 132 | CITRUS TRAINING | | 133 | CAPITAL PLANT LTD | | 134 | E-TOILET SERVICES LTD | | 135 | SECTION B | | 136 | GALLOPER WIND FARM LTD | | 137 | SECTION A | | 138 | SECTION C | | 139 | BACK ASSETS | | 140 | TAG FARNBOROUGH | | 141 | ALFIE BEST PROPERTY GROWTH | | 142 | STANSTED AEROSPACE | | 143 | NDS | | 144 | VOICE COMMUNICATIONS | | 145 | VAAROOM | | 146 | UK PROPERTIES MANAGEMENT | | 147 | UNIT 3 BURY FARM | | 148 | UNIT 19 BURY FARM | | 149 | UNIT 20 BURY FARM | | 150 | UNIT 1 BURY FARM | | 151 | UNIT 2 BURY FARM | | 152 | BAE SYSTEMS | | 153 | GARDNER TRAVEL | | 154 | VACANT OFFICES | | 155 | COACH STOP | | 156 | COBB SCAFFOLDING | | 157 | WESTBOUND SHIPPING LTD | | 158 | JOHN GOOD SHIPPING | | 159 | WINCANTON GROUP LTD | | 160 | STEDMAN GROUP OF COMPANIES | | 161 | A1 SCHMITZ CARGOBULL LTD | | 162 | P & B MOTORS | | 163 | SET IN STONE | | 164 | SHOEBURYNESS SORTING OFFICE | | 165 | R & Y TYERS REMOVALS | | 166 | WYVERN ENGINEERING SERVICE | | 167 | KNSI | | 168 | FOREMOST RESIN PROJECTS LTD | | 169 | DK STORAGE | | 170 | LUXURY TOILET HIRE UK LTD | | 171 | WESTONS PAVING SLABS | | 172 | CLARE NURSERY | | 173 | WESTSIDE DAVIDSON TRADING CO LTD | | 174 | KINGS (I P) LTD | | 175 | WESTON SIMFIRE | | 176 | CHANDELIER CLEANING SERVICES LTD | | 177 | R EMERY | | 178 | E J KENNERLEY & SON | | 179 | G W SMITH & SONS (BATTLESBRIDGE) LTD | | 180 | T BOATS | | 181 | CHELMSFORD CAR VALET SERVICE | | 182 | JEM ENGINEERING | | 183 | BARRY IVES HAULAGE LTD | | 184 | FAIRLOP CONCRETE | | 185 | T HAMMOND | | 186 | T R PRECISION ENGINEERING | | 187 | STUDIO ONE FRAMES LTD | | 188 | SAXON CONTRACTS LTD | | 189 | UNIT 1 | | 190 | UNIT 10 | | 191 | UNITS 19-20 | | 192 | LICNACITE (NORTH LONDON) LTD | | 193 | AJIMA LTD | | 194 | UNIT 5 | | 195 | A J DYKE & SONS LTD | | 196 | UNIT 2 | | 197 | UNIT 7 | | 198 | UNIT 11 | | 199 | UNIT 11B | | 200 | UNIT 2A KINGSTON FARM | | 201 | UNIT 11A | | 202 | DICOL UK | | 203 | AD THEOBOLD & SON | | 204 | WINE FANTASTIC | | 205 | ART GARDEN CENTRE | | 206 | BERNERS HALL FARM | | 207 | VALCO UK ENGINEERING LTD | | 208 | FORDS COACHES | | 209 | GREAT TOTHAM GARAGE | | 210 | CHAMOIS MOTORS | | 211 | LIBRA GRAPHIC DESIGN | | 212 | LIFESTILES LTD | | 213 | TRANSPORTER ENGINEERING LTD | | 214 | WILKIN & SONS LTD | | 215 | LIVERY AND ATKINSON LTD | | 216 | UNIT 8 | | 217 | ACRON TRUCK | | 218 | ACTION VEHICLES LTD | | 219 | TAYLOR BESPOKE LIMITED | | 220 | ALVANT MANAGEMENT LTD | | 221 | TAG AVIATION | | 222 | NORMAN ENGINEERING | | 223 | NORTH WEALD FLYING SERVICES LTD | | 224 | CYBERDERMIS | | 225 | R WOOD MOWERS | | 226 | UNITS 3 & 4 | | 227 | SMC TRADING | | 228 | A.WYLIE | | 229 | LODGE COACHES | | 230 | MMP | | 231 | EC GRAPHICS | | 232 | DANIEL CHISHOLM GROUNDWORKS | | 233 | CONFETTI & LACE | | 234 | ARRK EUROPE LTD | | 235 | NORTH WEALD SAW SERVICES | | 236 | ADVANCED TUITION CAR & SKID TRAINING SCHOOL | | 237 | LA ELECTRONICS | | 238 | ASSOCIATED JOINERY TECHNIQUES | | 239 | HARJO LTD | | 240 | THE TACK ROOM OFFICE | | 241 | THE BULL PEN OFFICE | | 242 | YOUR CHOICE | | 243 | BEE HOUSE | | 244 | N I READMAN | | 245 | MEATLINE LTD | | 246 | ELLIS CONTRACTORS | | 247 | 4 UTILITIES | | 248 | CLEANAWAY LTD | | 249 | THE COMMODITY CENTRE | | 250 | SERVICE TEAM | | 251 | ROCHFORD DISTRICT COUNCIL | | 252 | ANGLIAN AUTO RECOVERY | | 253 | PENMAN & GORE | | 254 | STATCO ELECTRONICS | | 255 | REALCOURT CONSTRUCTION LTD | | 256 | B H DENTAL LAB LTD | | 257 | UNITS S6 S7 S8 & S9 | | 258 | UNIT F17 | | 259 | UNITS S12 & S13 | | 260 | ACIT SOLUTIONS | | 261 | UNIT F19 | | 262 | HOLLAND PLASTICS LTD | | 263 | CKR SERVICES | | 264 | DIRECT MOVES | | 265 | DISTRICT BUS CO | | 266 | LOMBARD NORTH CENTRAL PLC | | 267 | S C GROVER LTD | | 268 | RECEPTION AREA + CONFERENCE SUITES | | 269 | GLOBAL GOLD | | 270 | MERIAL ANIMAL HEALTH | | 271 | INTERSIL | | 272 | COURTLAND WASTE MANAGEMENT | | 273 | MEDISECURE LIMITED | | 274 | WOLFELEC LIMITED | | 275 | GA PRECISION PRODUCTS | | 276 | ATS TYRES | | 277 | PAPER PAK | | 278 | GROVER TRANSPORT | | 279 | EBME LIMITED | | 280 | FABER & FABER LTD | | 281 | R F A SYSTEMS LTD | | 282 | B B & C FENCING LTD | | 283 | LIGHTER LIFE LIMITED | | 284 | SWR MOTORSPORTS | | 285 | RAMAR ENGINEERING LTD | | 286 | L S A HOLDINGS LTD | | 287 | EASTERN HARDWOODS LTD | | 288 | CROWN BROLAC | | 289 | INTERLINK | | 290 | A1 BACON CO LTD | | 291 | CD OFFICE INTERIORS LIMITED | | 292 | CJ PL NG | | 293 | AEC LIMITED | | 294 | MARLBOROUGH SURFACING | | 295 | LAND NORTH OF TARMACADAM PLANT | | 296 | FARM BUILDINGS | | 297 | UNIT 8B | | 298 | THE GARAGE | | 299 | S J RAYMENT | | 300 | ACCIDENT AND HEALTH UNDERWRITING LTD | | 301 | S & D PLANT & COMMERCIAL REPAIRS | | 302 | FINANCIAL VISION | | 303 | ACE OPTIONS | | 304 | VACANT PART | | 305 | CAPITALCLIFF LIMITED | | 306 | EUROSHUTTERS | | 307 | GO EXPRESS | | 308 | ABC TRANSMISSIONS | | 309 | THERMONIC CULTURE LIMITED | | 310 | ROGA KOPTYA LTD | | 311 | R & S AUTOS | | 312 | REPAIR & REFURB | | 313 | ABS TYRES | | 314 | ROVER TECH | | 315 | IPECO HOLDINGS | | 316 | EJS MOTORS | | 317 | H BALLARD | | 318 | WEST STREET VEHICLE DISMANTLERS | | 319 | S R TECHNICS | | 320 | ARGENT BUILDERS | | 321 | STORM AVIATION | | 322 | PLC HUNWICK LTD | | 323 | G M TRANSPORT (WALTHAM ABBEY) | | 324 | DOONES YARD | | 325 | MANSELL BONDED FABRICS | | 326 | H C R ELECTRONICS | | 327 | CLIFFS AUTOS | | 328 | GENERAL PORTFOLIO | | 329 | EVERARD OVENDON & CO LTD | | 330 | THE TRANSFORMER & ELECTRICAL CO LTD | | 331 | WILLIAM H JUDD & CO LTD | | 332 | PREMIER MOVING SERVICES | | 333 | MR A. SAMPSON | | 334 | UDC PLOT 12 | | 335 | UDC PLOT 14 | | 336 | STANFORD COACHWORKS & MOBILITY SERVICES LTD | | 337 | HALL & CO BUILDING MATERIALS CENTRE | | 338 | FUELLING SUPPORT LTD | | 339 | WESTBURY CONSERVATORIES | | 340 | C A BLACKWELL (CONTRACTS) | | 341 | AUDIX B B | | 342 | PORT LANE GARAGE | | 343 | SOUTH ESSEX STOCKHOLDERS LTD | | 344 | WOODLAND | | 345 | DC MORGAN & CO LTD | | 346 | BRAEFIELD PRECISION ENGINEERS | | 347 | TELTRONIC COMM LTD | | 348 | F J FRENCH LTD | | 349 | HARVEY BUSINESS PARK | | 350 | ERNEST DOE AND SONS | | 351 | SANDALS MOTORS (ESSEX) LTD | | 352 | NEW CONCEPT INTERIORS LTD | | 353 | BP OIL UK LTD | | 354 | BOSAL UK LTD | | 355 | SBC | | 356 | BLUNDEL WOODWORKER MACHINERY | | 357 | WILKS RUBBER PLASTICS LTD | | 358 | P H S LTD | | 359 | WALLEY LTD | | 360 | RUSH PARCEL SERVICES | | 361 | C M C FABRICATIONS | | 362 | TESCO PLC | | 363 | JET ONE X | | 364 | JENS AUTOS | | 365 | STOCK AUTO BREAKERS | | 366 | TEMPLE FARM SPARES | | 367 | MESSRS ASH KHAN & CO | | 368 | BLACK CROUCHMAN & CO | | 369 | TENANTS ASSOCIATION | | 370 | KINGSLEY (GRAYS) LTD | | 371 | JESSOPP AND GOUGH | | 372 | THE RONA PARTNERSHIP | | 373 | ACG PUBLICATIONS | | 374 | COLCHESTER CO-OPERATIVE SOCIETY | | 375 | LEE VALLEY REGIONAL PARK AUTHORITY | | 376 | CHARRINGTONS SOLID FUEL | | 377 | R HUTCHINGS | | 378 | MACS BUS HIRE | | 379 | TRINITY HOUSE LIGHTHOUSE SERVICE | | 380 | THE PARISH CLERK | | 381 | CIS LTD | | 382 | UDC PLOT 42 | | 383 | BARTLETT HAMMOND LTD | | 384 | COUNTY LIQUIDATIONS | | 385 | READY MIXED CONCRETE | | 386 | BATES TRAVELL SOLICITORS | | 387 | K TRAINING | | 388 | A B S INSURANCE AGENCY LTD | | 389 | UNIT 19C REAR | | 390 | MR PHIL TERRY | | 391 | SIMMONS AND SON | | 392 | GREAT EASTON FILLING STATION | | 393 | THE HILL COMPANY LTD | | 394 | A G CLAYDEN | | 395 | CENTURY HOUSE | | 396 | CMN LOGISTICS LTD | | 397 | LITTLEHAMPTON BOOK SERVICES LTD | | 398 | PROCTOR AND GAMBLE LTD | | 399 | OFFICES AT 2ND FLR RAEBURN HOUSE 1-3 | | 400 | S D SAMUELS LTD | | 401 | T G PENMAN & CO | | 402 | ST MODWEN PROPERTIES PLC | | 403 | OFFICE B | | 404 | HEAVYLIFT CARGO | | 405 | CHICK PRINTERS | | 406 | NETWORK 81 | | 407 | ECHO NEWSPAPERS | | 408 | YACHTING PRESS | | 409 | (VACANT) | | 410 | DEAD OR ALIVE | | 411 | MID ESSEX MIND | +-----+---------------------------------------------------+
#Function to categorize selected FirmNames and their counts in Processed_Data_VOA
def categorize_firm_name(name):
if isinstance(name, str):
name = name.upper()
if 'VACANT' in name:
return 'Vacant Property'
elif 'UNIT' in name:
return 'Unit or Office Space'
elif 'COUNCIL' in name:
return 'Government Agency'
elif 'LTD' in name or 'LIMITED' in name:
return 'Limited Company'
elif 'GARAGE' in name:
return 'Garage'
elif 'AUTO' in name or 'MOTORS' in name:
return 'Automotive'
elif 'FARM' in name:
return 'Farm'
elif 'NURSERY' in name:
return 'Nursery'
elif 'SCHOOL' in name:
return 'School'
elif 'STORE' in name or 'SHOP' in name:
return 'Retail'
elif 'OFFICE' in name:
return 'Office'
elif 'CLUB' in name:
return 'Club'
elif 'PARK' in name:
return 'Park'
elif 'CLINIC' in name:
return 'Clinic'
elif 'SOLICITORS' in name:
return 'Legal'
return 'Other'
#Apply categorization function to create 'Category' column
df_unique_firm_names['Category'] = df_unique_firm_names['FirmName'].apply(categorize_firm_name)
# Count occurrences of each category
category_counts = df_unique_firm_names['Category'].value_counts().reset_index()
category_counts.columns = ['Category', 'Count']
# Print tabulated results
from tabulate import tabulate
print(tabulate(category_counts, headers='keys', tablefmt='psql'))
+----+----------------------+---------+ | | Category | Count | |----+----------------------+---------| | 0 | Other | 220 | | 1 | Limited Company | 107 | | 2 | Unit or Office Space | 34 | | 3 | Automotive | 15 | | 4 | Office | 7 | | 5 | Garage | 6 | | 6 | Farm | 5 | | 7 | Vacant Property | 4 | | 8 | Government Agency | 4 | | 9 | Park | 4 | | 10 | Legal | 2 | | 11 | Nursery | 2 | | 12 | Club | 1 | | 13 | School | 1 | +----+----------------------+---------+
#Plot tabulated results of firm names and counts using interactive histogram
#Create function for plotting
category_counts_data = {
'Category': [
'Other', 'Limited Company', 'Unit or Office Space', 'Automotive', 'Office',
'Garage', 'Farm', 'Vacant Property', 'Government Agency', 'Park',
'Legal', 'Nursery', 'Club', 'School'
],
'Count': [
220, 107, 34, 15, 7, 6, 5, 4, 4, 4, 2, 2, 1, 1
]
}
#Create a DataFrame
category_counts_data_df = pd.DataFrame(category_counts_data)
#Plot the results
category_counts_data_df_plot = px.bar(
category_counts_data_df,
x='Category',
y='Count',
color='Category',
title='DISTRIBUTION TOP SELECTED FIRM NAMES AND COUNTS BY FIRM CATEGORY IN PROCESSED_DATA_VOA',
labels={'Count': 'Number of Firms', 'Category': 'Firm Category'},
hover_name='Category'
)
#Display the plot
category_counts_data_df_plot.show()
#Save the plot in the working folder
#Use write_image for saving plotly figures
category_counts_data_df_plot.write_image('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\DISTRIBUTION_TOP_SELECTED_FIRM_NAMES_AND_COUNTS_BY_FIRM_CATEGORY.png')
KEY INSIGHTS AND ANALYSIS: DISTRIBUTION OF TOP SELECTED FIRM NAMES AND COUNTS BY FIRM CATEGORY IN THE PROCESSED_DATA_VOA DATASET
1.OVERVIEW OF FIRM CATEGORY DISTRIBUTION
Analysis: The interactive bar chart illustrates the distribution of top selected firm names across various firm categories in the dataset which include:
Other Limited Company Unit or Office Space Automotive Office Garage Farm Vacant Property Government Agency Park Legal Nursery Club School
This chart shows a significant concentration of firms in the "Other" category, followed by "Limited Company" and "Unit or Office Space."
Key Insight: The dominance of the "Other" category indicates a large number of firms that do not fit neatly into the predefined categories, suggesting a diverse and possibly fragmented business landscape. The high count of "Limited Company" firms reflects a common legal structure for businesses, which could indicate a preference for limited liability and formal corporate governance among the firms in this dataset.
2.CONCENTRATION OF FIRMS IN SPECIFIC CATEGORIES
Analysis: The interactive visualization shows that after the "Other" category, "Limited Company" is the next most common category, followed by "Unit or Office Space." These three categories together account for the majority of firms in the dataset, while categories like "School," "Club," and "Nursery" have minimal representation.
Key Insight: The prominence of "Limited Company" firms highlights a trend toward formal business structures, which could be associated with greater business stability and access to financing. The lower counts in categories like "School" and "Nursery" might indicate fewer educational institutions or childcare facilities, or that these categories are less relevant in the context of the dataset.
3.ECONOMIC IMPLICATIONS
Analysis: The distribution of firms across categories can provide insights into the economic composition of the business area. A high number of firms in "Unit or Office Space" suggests a demand for commercial real estate, while the presence of categories like "Garage" and "Automotive" points to the importance of the automotive industry in the region.
Key Insight: The data suggests a diverse economic environment with a mix of service-oriented businesses ("Office," "Unit or Office Space") and more specialized categories ("Automotive," "Garage"). This diversity could imply a robust economy with various sectors contributing to local economic activity.
4.STRATEGIC INSIGHTS FOR STAKEHOLDERS
For Businesses: understanding the distribution of firms by category can help new businesses identify potential competitors and assess market saturation in different sectors.
For Investors: the concentration in categories like "Limited Company" and "Unit or Office Space" may indicate areas of stable investment with established businesses that prefer formal structures and commercial spaces.
For Policy Makers: the large number of firms in the "Other" category suggests a need for further categorization or support for diverse business types that do not fall into conventional classifications. Additionally, the low numbers in the "School" and "Nursery" categories may highlight potential areas for community development or investment.
DATA APPLICATIONS:
Market Analysis: Companies can use this data to identify potential market gaps or oversaturated categories, aiding in strategic planning and decision-making process.
Urban Planning: The insights gained from the distribution of firms can inform urban planning and zoning decisions, ensuring that infrastructure and services align with the needs of local businesses.
Economic Development: Local authorities and economic development agencies can leverage this information to support business growth in underrepresented categories or to attract firms that diversify the economic base.
This interactive bar chart analysis offers a comprehensive view of how firm names and categories are distributed in the dataset, providing valuable insights into the economic landscape and potential areas of opportunity for businesses and policymakers.
LONGEST OCCUPANCY DURATION:
#Convert FromDate and ToDate to datetime format
Processed_Data_VOA['FromDate'] = pd.to_datetime(Processed_Data_VOA['FromDate'], format='%Y-%m-%d')
Processed_Data_VOA['ToDate'] = pd.to_datetime(Processed_Data_VOA['ToDate'], format='%Y-%m-%d')
#calculate occupancy duration
Processed_Data_VOA['Occupancy_Duration'] = (Processed_Data_VOA['ToDate'] - Processed_Data_VOA['FromDate']).dt.days
#Derive DateOccupied and DateVacated from FromDate and ToDate
Processed_Data_VOA['DateOccupied'] = Processed_Data_VOA['FromDate']
Processed_Data_VOA['DateVacated'] = Processed_Data_VOA['ToDate']
#Convert to datetime format
Processed_Data_VOA['DateOccupied'] = pd.to_datetime(Processed_Data_VOA['DateOccupied'], format='%Y-%m-%d')
Processed_Data_VOA['DateVacated'] = pd.to_datetime(Processed_Data_VOA['DateVacated'], format='%Y-%m-%d')
#Find the longest occupancy duration from the calculated Occupancy_Duration
longest_occupancy = Processed_Data_VOA.loc[Processed_Data_VOA['Occupancy_Duration'].idxmax()]
#Displaying the details of the longest occupancy
longest_occupancy_data = [{"Attribute": "Firm Name", "Value": longest_occupancy['FirmName']},
{"Attribute": "Business Type", "Value": longest_occupancy['PriDescText']},
{"Attribute": "Date Occupied", "Value": longest_occupancy['DateOccupied'].date()},
{"Attribute": "Date Vacated", "Value": longest_occupancy['DateVacated'].date()},
{"Attribute": "Occupancy Duration (days)", "Value": longest_occupancy['Occupancy_Duration']}]
#Print details of the longest occupancy using tabulate
print("DESCRIPTIVE DETAILS OF THE LONGEST OCCUPANCY OF BUSINESS ACCOMODATION AND THE DURATION OF STAY IN THE PROCESSED_DATA_VOA DATASET:")
print(tabulate(longest_occupancy_data, headers='keys', tablefmt='psql'))
DESCRIPTIVE DETAILS OF THE LONGEST OCCUPANCY OF BUSINESS ACCOMODATION AND THE DURATION OF STAY IN THE PROCESSED_DATA_VOA DATASET: +---------------------------+----------------------+ | Attribute | Value | |---------------------------+----------------------| | Firm Name | The Occupier | | Business Type | Factory And Premises | | Date Occupied | 2009-09-14 | | Date Vacated | 2017-03-31 | | Occupancy Duration (days) | 2755 | +---------------------------+----------------------+
#Interactive plot for the distribution of longest occupancy duration
Plot_distribution_of_occupancy_durations = px.histogram(
Processed_Data_VOA,
x='Occupancy_Duration',
nbins=50,
title='THE LONGEST OCCUPANCY DURATION OF BUSINESS ACCOMODATION IN PROCESSED_DATA_VOA',
color_discrete_sequence=['#636EFA'],
template='plotly_white'
)
#Add annotation for the longest occupancy
Plot_distribution_of_occupancy_durations.add_annotation(
x=longest_occupancy['Occupancy_Duration'],
y=Processed_Data_VOA['Occupancy_Duration'].value_counts().max(),
text=f"Longest Occupancy: {longest_occupancy['Occupancy_Duration']} days<br>"
f"Firm Name: {longest_occupancy['FirmName']}<br>"
f"Business Type: {longest_occupancy['PriDescText']}<br>"
f"Date Occupied: {longest_occupancy['DateOccupied'].date()}<br>"
f"Date Vacated: {longest_occupancy['DateVacated'].date()}",
showarrow=True,
arrowhead=2,
ax=-50,
ay=-50,
bordercolor="#c7c7c7",
borderwidth=2,
borderpad=4,
bgcolor="white",
opacity=0.8
)
#Update layout for the plot
Plot_distribution_of_occupancy_durations.update_layout(
title_font_size=20,
xaxis_title='Longest Occupancy Duration (days)',
yaxis_title='Count',
bargap=0.2
)
#Display the plot
Plot_distribution_of_occupancy_durations.show()
#Save the plot in the working folder
#Use write_image for saving plotly figures
Plot_distribution_of_occupancy_durations.write_image('C:\\Users\\MY COMPUTER\\OneDrive\\Desktop\\DISSERTATION\\THE_LONGEST_OCCUPANCY_DURATION_OF_BUSINESS_ACCOMODATION.png')
KEY INSIGHTS AND ANALYSIS: LONGEST OCCUPANCY DURATION OF BUSINESS ACCOMMODATIONS IN THE PROCESSED_DATA_VOA DATASET
1.OVERVIEW OF OCCUPANCY DURATION
Analysis: The interactive histogram displays the distribution of the longest occupancy durations (in days) for business accommodations. The distribution is right-skewed, with a majority of accommodations having shorter occupancy durations. There are noticeable spikes at specific intervals in the dataset, particularly around 1000 days, 2000 days, and 2500 days.
Key Insight: The right-skewed nature of the interactive histogram suggests that while most businesses occupy premises for shorter periods, there are notable exceptions where firms maintain occupancy for extended durations, possibly due to long-term leases or ownership.
2.SIGNIFICANT OCCUPANCY SPIKES
Analysis: There are clear peaks in the histogram at around 1000 days, 2000 days, and 2500 days, indicating common durations of occupancy of business accommodations. These peaks may correspond to specific lease terms or business cycles that encourage firms to stay in their locations for these lengths of time.
Key Insight: The clustering around these specific durations could indicate standard lease agreements in the region, where businesses often commit to properties for these durations. This information could be valuable for property managers and real estate developers when planning lease terms.
3.MAXIMUM OCCUPANCY DURATION
Analysis: The tooltip in the chart highlights the longest recorded occupancy of business accommodation duration of 2755 days, associated with a firm named "The Occupier" in a "Factory and Premises" type of business. This duration reflects a long-term commitment to a particular property.
Key Insight: The longest occupancy duration of 2755 days (approximately 7.5 years) may suggest that certain types of businesses, such as manufacturing or industrial firms, require longer-term stability in their operations, leading them to occupy properties for extended periods.
4.IMPLICATIONS FOR REAL ESTATE PLANNING
For Property Developers: The concentration of occupancy durations of business accommodations around specific time frames suggests that developers might focus on offering leases with these durations to match market demand.
For Business Tenants: Understanding common occupancy durations can help businesses plan their long-term strategies regarding property usage, including lease negotiations and potential property investments.
For Investors: The data indicates that certain business types in the dataset, particularly those associated with longer occupancy durations, may offer more stable and long-term returns on property investments.
DATA APPLICATIONS:
Lease Structuring: Real estate managers and landlords can use this information to structure leases that align with common occupancy trends of business accommodations, potentially reducing turnover and vacancy rates.
Business Planning: Companies can consider these occupancy trends when planning their real estate strategies, ensuring that their lease terms align with industry standards or specific business needs.
Economic Development: Policymakers can leverage this data to understand the stability and longevity of businesses in their regions, which can inform decisions about infrastructure investments and support services.
This analysis of the longest occupancy durations interactive visualization provides valuable insights into business stability, lease structuring, and real estate planning, highlighting key patterns that can guide decision-making for a range of stakeholders in the property and business sectors.
STATISTICAL SUMMARY ANALYSIS FOR IDENTIFYING THE LONGEST OCCUPANCY OF BUSINESS ACCOMMODATION IN THE PROCESSED_DATA_VOA DATASET:
This analysis provides interactive and descriptive insights into the occupancy durations of business accommodations, identifying the firm with the longest stay in the dataset. The interactive visualizations plotted effectively displays the distribution of the firm categories and the occupancy durations.
KEY FINDINGS: Unique Firm Names Unique firm names are extracted using the "FirmName" column of the dataset.
Categorization of Firm Names: a function is defined to categorize the firm names extracted from the Processed_Data_VOA into various business types for better readability.
Category Counts: the categorized firm names are counted, resulting in a distribution of firm categories:
Other(used to categorize other firm names aside from the major firms grouped in the dataset) with 220 business accommodations.
Limited Company with 107 business accommodations.
Unit or Office Space with 34 business accomodations.
Automotive with 15 business accommodations.
Office with 7 business accommodations.
Garage with 6 business accommodations.
Farm with 5 business accommodations.
Vacant Property with 4 business accommodations.
Government Agency with 4 business accommodations.
Park with 4 business accommodations.
Legal with 2 business accommodations.
Nursery with 2 business accommodations.
Club with 1 business accommodation.
School with 1 business accommodation.
DATA VISUALIZATION:
write_image is used to save the plot to the designated working environment.
Firm Category Distribution Plot: an interactive bar plot is created to visualize the distribution of the top-selected firm names and their counts by firm category in the dataset.
LONGEST OCCUPANCY ANALYSIS:
Calculation of Occupancy Duration:
FromDate and ToDate columns are converted to datetime format.
FromDate is subtracted from ToDate to calculate the occupancy duration record.
New columns DateOccupied and DateVacated are derived from existing columns FromDate and ToDate.
Finding Longest Occupancy:
The record with the longest occupancy duration in the dataset is identified as:
Firm Name: The Occupier
Business Type: Factory And Premises
Date Occupied: 2009-09-14
Date Vacated: 2017-03-31
Occupancy Duration: 2755 days
Interactive and descriptive visualization to plot the record of the longest occupancy duration using a histogram plot to show the distribution of the firm with the longest occupancy duration.